Convergent evolution

From Wikipedia, the free encyclopedia

https://en.wikipedia.org/wiki/Convergent_evolution 

 

Two succulent plant genera, Euphorbia and Astrophytum, are only distantly related, but the species within each have converged on a similar body form 

 

Convergent evolution is the independent evolution of similar features in species of different periods or epochs in time. Convergent evolution creates analogous structures that have similar form or function but were not present in the last common ancestor of those groups. The cladistic term for the same phenomenon is homoplasy. The recurrent evolution of flight is a classic example, as flying insects, birds, pterosaurs, and bats have independently evolved the useful capacity of flight. Functionally similar features that have arisen through convergent evolution are analogous, whereas homologous structures or traits have a common origin but can have dissimilar functions. Bird, bat, and pterosaur wings are analogous structures, but their forelimbs are homologous, sharing an ancestral state despite serving different functions.

The opposite of convergent evolution is divergent evolution, where related species evolve different traits. Convergent evolution is similar to parallel evolution, which occurs when two independent species evolve in the same direction and thus independently acquire similar characteristics; for instance, gliding frogs have evolved in parallel from multiple types of tree frog.

Many instances of convergent evolution are known in plants, including the repeated development of C₄ photosynthesis, seed dispersal by fleshy fruits adapted to be eaten by animals, and carnivory.

Overview

Homology and analogy in mammals and insects: on the horizontal axis, the structures are homologous in morphology, but different in function due to differences in habitat. On the vertical axis, the structures are analogous in function due to similar lifestyles but anatomically different with different phylogeny.

Further information: List of examples of convergent evolution

In morphology, analogous traits arise when different species live in similar ways and/or a similar environment, and so face the same environmental factors. When occupying similar ecological niches (that is, a distinctive way of life) similar problems can lead to similar solutions. The British anatomist Richard Owen was the first to identify the fundamental difference between analogies and homologies.

In biochemistry, physical and chemical constraints on mechanisms have caused some active site arrangements such as the catalytic triad to evolve independently in separate enzyme superfamilies.

In his 1989 book Wonderful Life, Stephen Jay Gould argued that if one could "rewind the tape of life [and] the same conditions were encountered again, evolution could take a very different course." Simon Conway Morris disputes this conclusion, arguing that convergence is a dominant force in evolution, and given that the same environmental and physical constraints are at work, life will inevitably evolve toward an "optimum" body plan, and at some point, evolution is bound to stumble upon intelligence, a trait presently identified with at least primates, corvids, and cetaceans.

Distinctions

Cladistics

Further information: Cladistics

In cladistics, a homoplasy is a trait shared by two or more taxa for any reason other than that they share a common ancestry. Taxa which do share ancestry are part of the same clade; cladistics seeks to arrange them according to their degree of relatedness to describe their phylogeny. Homoplastic traits caused by convergence are therefore, from the point of view of cladistics, confounding factors which could lead to an incorrect analysis.

Atavism

Further information: Atavism

It can be difficult to tell whether a trait has been lost and then re-evolved convergently, or whether a gene has simply been switched off and then re-enabled later. Such a re-emerged trait is called an atavism. From a mathematical standpoint, an unused gene (selectively neutral) has a steadily decreasing probability of retaining potential functionality over time. The time scale of this process varies greatly in different phylogenies; in mammals and birds, there is a reasonable probability of a gene's remaining in the genome in a potentially functional state for around 6 million years.

Parallel vs. convergent evolution

Evolution at an amino acid position. In each case, the left-hand species changes from having alanine (A) at a specific position in a protein in a hypothetical ancestor, and now has serine (S) there. The right-hand species may undergo divergent, parallel, or convergent evolution at this amino acid position relative to the first species.

When two species are similar in a particular character, evolution is defined as parallel if the ancestors were also similar, and convergent if they were not. Some scientists have argued that there is a continuum between parallel and convergent evolution, while others maintain that despite some overlap, there are still important distinctions between the two.

When the ancestral forms are unspecified or unknown, or the range of traits considered is not clearly specified, the distinction between parallel and convergent evolution becomes more subjective. For instance, the striking example of similar placental and marsupial forms is described by Richard Dawkins in The Blind Watchmaker as a case of convergent evolution, because mammals on each continent had a long evolutionary history prior to the extinction of the dinosaurs under which to accumulate relevant differences.

At molecular level

Evolutionary convergence of serine and cysteine protease towards the same catalytic triads organisation of acid-base-nucleophile in different protease superfamilies. Shown are the triads of subtilisin, prolyl oligopeptidase, TEV protease, and papain.

Proteins

Tertiary structures

Many proteins share analogous structural elements that arose independently across different genomes. There are several examples of convergent protein motifs sharing similar arrangements of structural elements. Whole protein structures too have arisen through convergent evolution.

Protease active sites

The enzymology of proteases provides some of the clearest examples of convergent evolution. These examples reflect the intrinsic chemical constraints on enzymes, leading evolution to converge on equivalent solutions independently and repeatedly.

Serine and cysteine proteases use different amino acid functional groups (alcohol or thiol) as a nucleophile. To activate that nucleophile, they orient an acidic and a basic residue in a catalytic triad. The chemical and physical constraints on enzyme catalysis have caused identical triad arrangements to evolve independently more than 20 times in different enzyme superfamilies.

Threonine proteases use the amino acid threonine as their catalytic nucleophile. Unlike cysteine and serine, threonine is a secondary alcohol (i.e. has a methyl group). The methyl group of threonine greatly restricts the possible orientations of triad and substrate, as the methyl clashes with either the enzyme backbone or the histidine base. Consequently, most threonine proteases use an N-terminal threonine in order to avoid such steric clashes. Several evolutionarily independent enzyme superfamilies with different protein folds use the N-terminal residue as a nucleophile. This commonality of active site but difference of protein fold indicates that the active site evolved convergently in those families.

Cone snail and fish insulin

Conus geographus produces a distinct form of insulin that is more similar to fish insulin protein sequences than to insulin from more closely related molluscs, suggesting convergent evolution, though with the possibility of horizontal gene transfer.

Ferrous iron uptake via protein transporters in land plants and chlorophytes

Distant homologues of the metal ion transporters ZIP in land plants and chlorophytes have converged in structure, likely to take up Fe²⁺ efficiently. The IRT1 proteins from Arabidopsis thaliana and rice have extremely different amino acid sequences from Chlamydomonas's IRT1, but their three-dimensional structures are similar, suggesting convergent evolution.

Na⁺,K⁺-ATPase and Insect resistance to cardiotonic steroids

Many examples of convergent evolution exist in insects in terms of developing resistance at a molecular level to toxins. One well-characterized example is the evolution of resistance to cardiotonic steroids (CTSs) via amino acid substitutions at well-defined positions of the α-subunit of Na⁺,K⁺-ATPase (ATPalpha). Variation in ATPalpha has been surveyed in various CTS-adapted species spanning six insect orders. Among 21 CTS-adapted species, 58 (76%) of 76 amino acid substitutions at sites implicated in CTS resistance occur in parallel in at least two lineages. 30 of these substitutions (40%) occur at just two sites in the protein (positions 111 and 122). CTS-adapted species have also recurrently evolved neo-functionalized duplications of ATPalpha, with convergent tissue-specific expression patterns.

Nucleic acids

Convergence occurs at the level of DNA and the amino acid sequences produced by translating structural genes into proteins. Studies have found convergence in amino acid sequences in echolocating bats and the dolphin; among marine mammals; between giant and red pandas; and between the thylacine and canids. Convergence has also been detected in a type of non-coding DNA, cis-regulatory elements, such as in their rates of evolution; this could indicate either positive selection or relaxed purifying selection.

In animals

Dolphins and ichthyosaurs converged on many adaptations for fast swimming.

Bodyplans

Swimming animals including fish such as herrings, marine mammals such as dolphins, and ichthyosaurs (of the Mesozoic) all converged on the same streamlined shape. A similar shape and swimming adaptations are even present in molluscs, such as Phylliroe. The fusiform bodyshape (a tube tapered at both ends) adopted by many aquatic animals is an adaptation to enable them to travel at high speed in a high drag environment. Similar body shapes are found in the earless seals and the eared seals: they still have four legs, but these are strongly modified for swimming.

The marsupial fauna of Australia and the placental mammals of the Old World have several strikingly similar forms, developed in two clades, isolated from each other. The body, and especially the skull shape, of the thylacine (Tasmanian tiger or Tasmanian wolf) converged with those of Canidae such as the red fox, Vulpes vulpes.

• Convergence of marsupial and placental mammals
• 
  Red fox skeleton
  
  
• 
  Skulls of thylacine (left), timber wolf (right)
   
• 
  Thylacine skeleton

Echolocation

As a sensory adaptation, echolocation has evolved separately in cetaceans (dolphins and whales) and bats, but from the same genetic mutations.

Electric fishes

The Gymnotiformes of South America and the Mormyridae of Africa independently evolved passive electroreception (around 119 and 110 million years ago, respectively). Around 20 million years after acquiring that ability, both groups evolved active electrogenesis, producing weak electric fields to help them detect prey.

• Convergence of weakly electric fishes
• 
  A gymnotiform electrolocation waveform
  
  
• 
  A gymnotiform electric fish of South America
• 
  A mormyrid electric fish of Africa
   
• 
  A mormyrid electrolocation waveform

Eyes

Main article: Eye evolution

The camera eyes of vertebrates (left) and cephalopods (right) developed independently and are wired differently; for instance, optic nerve ⁽³⁾ fibres ⁽²⁾ reach the vertebrate retina ⁽¹⁾ from the front, creating a blind spot ⁽⁴⁾.

One of the best-known examples of convergent evolution is the camera eye of cephalopods (such as squid and octopus), vertebrates (including mammals) and cnidarians (such as jellyfish). Their last common ancestor had at most a simple photoreceptive spot, but a range of processes led to the progressive refinement of camera eyes—with one sharp difference: the cephalopod eye is "wired" in the opposite direction, with blood and nerve vessels entering from the back of the retina, rather than the front as in vertebrates. As a result, vertebrates have a blind spot.

Sex organs

Hydrostatic penises have convergently evolved at least six times in male amniotes. In these species, males copulate with females and internally fertilize their eggs. Similar intromittent organs have evolved in invertebrates such as octopuses and gastropods.

Flight

Further information: Flying and gliding animals § Evolution and ecology of aerial locomotion

Vertebrate wings are partly homologous (from forelimbs), but analogous as organs of flight in (1) pterosaurs, (2) bats, (3) birds, evolved separately.

Birds and bats have homologous limbs because they are both ultimately derived from terrestrial tetrapods, but their flight mechanisms are only analogous, so their wings are examples of functional convergence. The two groups have independently evolved their own means of powered flight. Their wings differ substantially in construction. The bat wing is a membrane stretched across four extremely elongated fingers and the legs. The airfoil of the bird wing is made of feathers, strongly attached to the forearm (the ulna) and the highly fused bones of the wrist and hand (the carpometacarpus), with only tiny remnants of two fingers remaining, each anchoring a single feather. So, while the wings of bats and birds are functionally convergent, they are not anatomically convergent. Birds and bats also share a high concentration of cerebrosides in the skin of their wings. This improves skin flexibility, a trait useful for flying animals; other mammals have a far lower concentration. The extinct pterosaurs independently evolved wings from their fore- and hindlimbs, while insects have wings that evolved separately from different organs.

Flying squirrels and sugar gliders are much alike in their mammalian body plans, with gliding wings stretched between their limbs, but flying squirrels are placentals while sugar gliders are marsupials, widely separated within the mammal lineage from the placentals.

Hummingbird hawk-moths and hummingbirds have evolved similar flight and feeding patterns.

Insect mouthparts

Insect mouthparts show many examples of convergent evolution. The mouthparts of different insect groups consist of a set of homologous organs, specialised for the dietary intake of that insect group. Convergent evolution of many groups of insects led from original biting-chewing mouthparts to different, more specialised, derived function types. These include, for example, the proboscis of flower-visiting insects such as bees and flower beetles, or the biting-sucking mouthparts of blood-sucking insects such as fleas and mosquitos.

Intelligence

Further information: Cephalopod intelligence

Advanced intelligence has evolved independently in cephalopods and vertebrates. Octopus have demonstrated mammalian levels of problem-solving, cognition, and learning behaviors. One aquarium director even claimed his octopus specimen to have developed a sense of personal taste as to the arrangement of its tank. Unlike other highly intelligent animals, cephalopods typically live short lives with varying levels of sociality, with the bulk of the nervous system divided between the head and limbs.

Opposable thumbs

Opposable thumbs allowing the grasping of objects are most often associated with primates, like humans and other apes, monkeys, and lemurs. Opposable thumbs also evolved in giant pandas, but these are completely different in structure, having six fingers including the thumb, which develops from a wrist bone entirely separately from other fingers.

Primate phenotypes

Further information: Human skin color § Genetics of skin color variation

 

Convergent evolution in humans includes blue eye colour and light skin colour. When humans migrated out of Africa, they moved to more northern latitudes with less intense sunlight. It was beneficial to them to have reduced skin pigmentation. It appears certain that there was some lightening of skin colour before European and East Asian lineages diverged, as there are some skin-lightening genetic differences that are common to both groups. However, after the lineages diverged and became genetically isolated, the skin of both groups lightened more, and that additional lightening was due to different genetic changes.

Despite the similarity of appearance, the genetic basis of blue eyes is different in humans and lemurs.

Lemurs and humans are both primates. Ancestral primates had brown eyes, as most primates do today. The genetic basis of blue eyes in humans has been studied in detail and much is known about it. It is not the case that one gene locus is responsible, say with brown dominant to blue eye colour. However, a single locus is responsible for about 80% of the variation. In lemurs, the differences between blue and brown eyes are not completely known, but the same gene locus is not involved.

In plants

In myrmecochory, seeds such as those of Chelidonium majus have a hard coating and an attached oil body, an elaiosome, for dispersal by ants.

The annual life-cycle

While most plant species are perennial, about 6% follow an annual life cycle, living for only one growing season. The annual life cycle independently emerged in over 120 plant families of angiosperms. The prevalence of annual species increases under hot-dry summer conditions in the four species-rich families of annuals (Asteraceae, Brassicaceae, Fabaceae, and Poaceae), indicating that the annual life cycle is adaptive.

Carbon fixation

C₄ photosynthesis, one of the three major carbon-fixing biochemical processes, has arisen independently up to 40 times. About 7,600 plant species of angiosperms use C₄ carbon fixation, with many monocots including 46% of grasses such as maize and sugar cane, and dicots including several species in the Chenopodiaceae and the Amaranthaceae.

Fruits

Fruits with a wide variety of structural origins have converged to become edible. Apples are pomes with five carpels; their accessory tissues form the apple's core, surrounded by structures from outside the botanical fruit, the receptacle or hypanthium. Other edible fruits include other plant tissues; the fleshy part of a tomato is the walls of the pericarp. This implies convergent evolution under selective pressure, in this case the competition for seed dispersal by animals through consumption of fleshy fruits.

Seed dispersal by ants (myrmecochory) has evolved independently more than 100 times, and is present in more than 11,000 plant species. It is one of the most dramatic examples of convergent evolution in biology.

Carnivory

Molecular convergence in carnivorous plants

Carnivory has evolved multiple times independently in plants in widely separated groups. In three species studied, Cephalotus follicularis, Nepenthes alata and Sarracenia purpurea, there has been convergence at the molecular level. Carnivorous plants secrete enzymes into the digestive fluid they produce. By studying phosphatase, glycoside hydrolase, glucanase, RNAse and chitinase enzymes as well as a pathogenesis-related protein and a thaumatin-related protein, the authors found many convergent amino acid substitutions. These changes were not at the enzymes' catalytic sites, but rather on the exposed surfaces of the proteins, where they might interact with other components of the cell or the digestive fluid. The authors also found that homologous genes in the non-carnivorous plant Arabidopsis thaliana tend to have their expression increased when the plant is stressed, leading the authors to suggest that stress-responsive proteins have often been co-opted in the repeated evolution of carnivory.

Methods of inference

Angiosperm phylogeny of orders based on classification by the Angiosperm Phylogeny Group. The figure shows the number of inferred independent origins of C₃-C₄ photosynthesis and C₄ photosynthesis in parentheses.

Phylogenetic reconstruction and ancestral state reconstruction proceed by assuming that evolution has occurred without convergence. Convergent patterns may, however, appear at higher levels in a phylogenetic reconstruction, and are sometimes explicitly sought by investigators. The methods applied to infer convergent evolution depend on whether pattern-based or process-based convergence is expected. Pattern-based convergence is the broader term, for when two or more lineages independently evolve patterns of similar traits. Process-based convergence is when the convergence is due to similar forces of natural selection.

Pattern-based measures

Earlier methods for measuring convergence incorporate ratios of phenotypic and phylogenetic distance by simulating evolution with a Brownian motion model of trait evolution along a phylogeny. More recent methods also quantify the strength of convergence. One drawback to keep in mind is that these methods can confuse long-term stasis with convergence due to phenotypic similarities. Stasis occurs when there is little evolutionary change among taxa.

Distance-based measures assess the degree of similarity between lineages over time. Frequency-based measures assess the number of lineages that have evolved in a particular trait space.

Process-based measures

Methods to infer process-based convergence fit models of selection to a phylogeny and continuous trait data to determine whether the same selective forces have acted upon lineages. This uses the Ornstein–Uhlenbeck process to test different scenarios of selection. Other methods rely on an a priori specification of where shifts in selection have occurred.

Distributed artificial intelligence

From Wikipedia, the free encyclopedia

https://en.wikipedia.org/wiki/Distributed_artificial_intelligence 

Distributed artificial intelligence (DAI) also called Decentralized Artificial Intelligence is a subfield of artificial intelligence research dedicated to the development of distributed solutions for problems. DAI is closely related to and a predecessor of the field of multi-agent systems.

Multi-agent systems and distributed problem solving are the two main DAI approaches. There are numerous applications and tools.

Definition

Distributed Artificial Intelligence (DAI) is an approach to solving complex learning, planning, and decision-making problems. It is embarrassingly parallel, thus able to exploit large scale computation and spatial distribution of computing resources. These properties allow it to solve problems that require the processing of very large data sets. DAI systems consist of autonomous learning processing nodes (agents), that are distributed, often at a very large scale. DAI nodes can act independently, and partial solutions are integrated by communication between nodes, often asynchronously. By virtue of their scale, DAI systems are robust and elastic, and by necessity, loosely coupled. Furthermore, DAI systems are built to be adaptive to changes in the problem definition or underlying data sets due to the scale and difficulty in redeployment.

DAI systems do not require all the relevant data to be aggregated in a single location, in contrast to monolithic or centralized Artificial Intelligence systems which have tightly coupled and geographically close processing nodes. Therefore, DAI systems often operate on sub-samples or hashed impressions of very large datasets. In addition, the source dataset may change or be updated during the course of the execution of a DAI system.

Development

In 1975 distributed artificial intelligence emerged as a subfield of artificial intelligence that dealt with interactions of intelligent agents. Distributed artificial intelligence systems were conceived as a group of intelligent entities, called agents, that interacted by cooperation, by coexistence or by competition. DAI is categorized into multi-agent systems and distributed problem solving. In multi-agent systems the main focus is how agents coordinate their knowledge and activities. For distributed problem solving the major focus is how the problem is decomposed and the solutions are synthesized.

Goals

The objectives of Distributed Artificial Intelligence are to solve the reasoning, planning, learning and perception problems of artificial intelligence, especially if they require large data, by distributing the problem to autonomous processing nodes (agents). To reach the objective, DAI requires:

• A distributed system with robust and elastic computation on unreliable and failing resources that are loosely coupled
• Coordination of the actions and communication of the nodes
• Subsamples of large data sets and online machine learning

There are many reasons for wanting to distribute intelligence or cope with multi-agent systems. Mainstream problems in DAI research include the following:

• Parallel problem solving: mainly deals with how classic artificial intelligence concepts can be modified, so that multiprocessor systems and clusters of computers can be used to speed up calculation.
• Distributed problem solving (DPS): the concept of agent, autonomous entities that can communicate with each other, was developed to serve as an abstraction for developing DPS systems. See below for further details.
• Multi-Agent Based Simulation (MABS): a branch of DAI that builds the foundation for simulations that need to analyze not only phenomena at macro level but also at micro level, as it is in many social simulation scenarios.

Approaches

Two types of DAI has emerged:

• In Multi-agent systems agents coordinate their knowledge and activities and reason about the processes of coordination. Agents are physical or virtual entities that can act, perceive its environment and communicate with other agents. The agent is autonomous and has skills to achieve goals. The agents change the state of their environment by their actions. There are a number of different coordination techniques.
• In distributed problem solving the work is divided among nodes and the knowledge is shared. The main concerns are task decomposition and synthesis of the knowledge and solutions.

DAI can apply a bottom-up approach to AI, similar to the subsumption architecture as well as the traditional top-down approach of AI. In addition, DAI can also be a vehicle for emergence.

Challenges

The challenges in Distributed AI are:

1. How to carry out communication and interaction of agents and which communication language or protocols should be used.
2. How to ensure the coherency of agents.
3. How to synthesise the results among 'intelligent agents' group by formulation, description, decomposition and allocation.

Applications and tools

Areas where DAI have been applied are:

• Electronic commerce, e.g. for trading strategies the DAI system learns financial trading rules from subsamples of very large samples of financial data
• Networks, e.g. in telecommunications the DAI system controls the cooperative resources in a WLAN network
• Routing, e.g. model vehicle flow in transport networks
• Scheduling, e.g. flow shop scheduling where the resource management entity ensures local optimization and cooperation for global and local consistency
• Search engines, e.g. in LLM federated search like Ithy where document retrieval and analysis are distributed to DAI agents before aggregation
• Multi-Agent systems, e.g. artificial life, the study of simulated life
• Electric power systems, e.g. Condition Monitoring Multi-Agent System (COMMAS) applied to transformer condition monitoring, and IntelliTEAM II Automatic Restoration System

DAI integration in tools has included:

• ECStar is a distributed rule-based learning system.

Agents

Systems: Agents and multi-agents

Further information: Multi-agent system § Applications

Notion of Agents: Agents can be described as distinct entities with standard boundaries and interfaces designed for problem solving.

Notion of Multi-Agents: Multi-Agent system is defined as a network of agents which are loosely coupled working as a single entity like society for problem solving that an individual agent cannot solve.

Software agents

The key concept used in DPS and MABS is the abstraction called software agents. An agent is a virtual (or physical) autonomous entity that has an understanding of its environment and acts upon it. An agent is usually able to communicate with other agents in the same system to achieve a common goal, that one agent alone could not achieve. This communication system uses an agent communication language.

A first classification that is useful is to divide agents into:

• reactive agent – A reactive agent is not much more than an automaton that receives input, processes it and produces an output.
• deliberative agent – A deliberative agent in contrast should have an internal view of its environment and is able to follow its own plans.
• hybrid agent – A hybrid agent is a mixture of reactive and deliberative, that follows its own plans, but also sometimes directly reacts to external events without deliberation.

Well-recognized agent architectures that describe how an agent is internally structured are:

• ASMO (emergence of distributed modules)
• BDI (Believe Desire Intention, a general architecture that describes how plans are made)
• InterRAP (A three-layer architecture, with a reactive, a deliberative and a social layer)
• PECS (Physics, Emotion, Cognition, Social, describes how those four parts influences the agents behavior).
• Soar (a rule-based approach)

Swarm intelligence

From Wikipedia, the free encyclopedia

https://en.wikipedia.org/wiki/Swarm_intelligence 

A flock of starlings reacting to a predator

Swarm intelligence (SI) is the collective behavior of decentralized, self-organized systems, natural or artificial. The concept is employed in work on artificial intelligence. The expression was introduced by Gerardo Beni and Jing Wang in 1989, in the context of cellular robotic systems.

Swarm intelligence systems consist typically of a population of simple agents or boids interacting locally with one another and with their environment. The inspiration often comes from nature, especially biological systems. The agents follow very simple rules, and although there is no centralized control structure dictating how individual agents should behave, local, and to a certain degree random, interactions between such agents lead to the emergence of "intelligent" global behavior, unknown to the individual agents. Examples of swarm intelligence in natural systems include ant colonies, bee colonies, bird flocking, hawks hunting, animal herding, bacterial growth, fish schooling and microbial intelligence.

The application of swarm principles to robots is called swarm robotics while swarm intelligence refers to the more general set of algorithms. Swarm prediction has been used in the context of forecasting problems. Similar approaches to those proposed for swarm robotics are considered for genetically modified organisms in synthetic collective intelligence.

Models of swarm behavior

See also: Swarm behaviour

Boids (Reynolds 1987)

Main article: Boids

Boids is an artificial life program, developed by Craig Reynolds in 1986, which simulates flocking. It was published in 1987 in the proceedings of the ACM SIGGRAPH conference. The name "boid" corresponds to a shortened version of "bird-oid object", which refers to a bird-like object.

As with most artificial life simulations, Boids is an example of emergent behavior; that is, the complexity of Boids arises from the interaction of individual agents (the boids, in this case) adhering to a set of simple rules. The rules applied in the simplest Boids world are as follows:

• separation: steer to avoid crowding local flockmates
• alignment: steer towards the average heading of local flockmates
• cohesion: steer to move toward the average position (center of mass) of local flockmates

More complex rules can be added, such as obstacle avoidance and goal seeking.

Self-propelled particles (Vicsek et al. 1995)

Main article: Self-propelled particles

Self-propelled particles (SPP), also referred to as the Vicsek model, was introduced in 1995 by Vicsek et al. as a special case of the boids model introduced in 1986 by Reynolds. A swarm is modelled in SPP by a collection of particles that move with a constant speed but respond to a random perturbation by adopting at each time increment the average direction of motion of the other particles in their local neighbourhood. SPP models predict that swarming animals share certain properties at the group level, regardless of the type of animals in the swarm. Swarming systems give rise to emergent behaviours which occur at many different scales, some of which are turning out to be both universal and robust. It has become a challenge in theoretical physics to find minimal statistical models that capture these behaviours.

Metaheuristics

See also: List of metaphor-based metaheuristics

Evolutionary algorithms (EA), particle swarm optimization (PSO), differential evolution (DE), ant colony optimization (ACO) and their variants dominate the field of nature-inspired metaheuristics. This list includes algorithms published up to circa the year 2000. A large number of more recent metaphor-inspired metaheuristics have started to attract criticism in the research community for hiding their lack of novelty behind an elaborate metaphor. For algorithms published since that time, see List of metaphor-based metaheuristics.

Metaheuristics lack a confidence in a solution. When appropriate parameters are determined, and when sufficient convergence stage is achieved, they often find a solution that is optimal, or near close to optimum – nevertheless, if one does not know optimal solution in advance, a quality of a solution is not known. In spite of this obvious drawback it has been shown that these types of algorithms work well in practice, and have been extensively researched, and developed. On the other hand, it is possible to avoid this drawback by calculating solution quality for a special case where such calculation is possible, and after such run it is known that every solution that is at least as good as the solution a special case had, has at least a solution confidence a special case had. One such instance is Ant-inspired Monte Carlo algorithm for Minimum Feedback Arc Set where this has been achieved probabilistically via hybridization of Monte Carlo algorithm with Ant Colony Optimization technique.

Ant colony optimization (Dorigo 1992)

Main article: Ant colony optimization

Ant colony optimization (ACO), introduced by Dorigo in his doctoral dissertation, is a class of optimization algorithms modeled on the actions of an ant colony. ACO is a probabilistic technique useful in problems that deal with finding better paths through graphs. Artificial 'ants'—simulation agents—locate optimal solutions by moving through a parameter space representing all possible solutions. Natural ants lay down pheromones directing each other to resources while exploring their environment. The simulated 'ants' similarly record their positions and the quality of their solutions, so that in later simulation iterations more ants locate for better solutions.

Particle swarm optimization (Kennedy, Eberhart & Shi 1995)

Main article: Particle swarm optimization

Particle swarm optimization (PSO) is a global optimization algorithm for dealing with problems in which a best solution can be represented as a point or surface in an n-dimensional space. Hypotheses are plotted in this space and seeded with an initial velocity, as well as a communication channel between the particles. Particles then move through the solution space, and are evaluated according to some fitness criterion after each timestep. Over time, particles are accelerated towards those particles within their communication grouping which have better fitness values. The main advantage of such an approach over other global minimization strategies such as simulated annealing is that the large number of members that make up the particle swarm make the technique impressively resilient to the problem of local minima.

Artificial bee colony algorithm (Karaboga 2005)

Main article: Artificial bee colony algorithm

Karaboga introduced ABC metaheuristic in 2005 as an answer to optimize numerical problems. Inspired by honey bee foraging behavior, Karaboga's model had three components. The employed, onlooker, and scout. In practice, the artificial scout bee would expose all food source positions (solutions) good or bad. The employed bee would search for the shortest route to each position to extract the food amount (quality) of the source. If the food was depleted from the source, the employed bee would become a scout and randomly search for other food sources. Each source that became abandoned created negative feedback meaning, the answers found were poor solutions. The onlooker bees wait for employed bees to either abandon a source or give information that the source has a large quantity of food and is worth sending additional resources to. The more an onlooker bee is recruited, the more positive the feedback is meaning that the answer is likely a good solution.

Artificial Swarm Intelligence (2015)

Artificial Swarm Intelligence (ASI) is method of amplifying the collective intelligence of networked human groups using control algorithms modeled after natural swarms. Sometimes referred to as Human Swarming or Swarm AI, the technology connects groups of human participants into real-time systems that deliberate and converge on solutions as dynamic swarms when simultaneously presented with a question ASI has been used for a wide range of applications, from enabling business teams to generate highly accurate financial forecasts to enabling sports fans to outperform Vegas betting markets. ASI has also been used to enable groups of doctors to generate diagnoses with significantly higher accuracy than traditional methods. ASI has been used by the Food and Agriculture Organization (FAO) of the United Nations to help forecast famines in hotspots around the world.

Applications

Swarm Intelligence-based techniques can be used in a number of applications. The U.S. military is investigating swarm techniques for controlling unmanned vehicles. The European Space Agency is thinking about an orbital swarm for self-assembly and interferometry. NASA is investigating the use of swarm technology for planetary mapping. A 1992 paper by M. Anthony Lewis and George A. Bekey discusses the possibility of using swarm intelligence to control nanobots within the body for the purpose of killing cancer tumors. Conversely al-Rifaie and Aber have used stochastic diffusion search to help locate tumours. Swarm intelligence (SI) is increasingly applied in Internet of Things (IoT) systems, and by association to Intent-Based Networking (IBN), due to its ability to handle complex, distributed tasks through decentralized, self-organizing algorithms. Swarm intelligence has also been applied for data mining and cluster analysis. Ant-based models are further subject of modern management theory.

Ant-based routing

The use of swarm intelligence in telecommunication networks has also been researched, in the form of ant-based routing. This was pioneered separately by Dorigo et al. and Hewlett-Packard in the mid-1990s, with a number of variants existing. Basically, this uses a probabilistic routing table rewarding/reinforcing the route successfully traversed by each "ant" (a small control packet) which flood the network. Reinforcement of the route in the forwards, reverse direction and both simultaneously have been researched: backwards reinforcement requires a symmetric network and couples the two directions together; forwards reinforcement rewards a route before the outcome is known (but then one would pay for the cinema before one knows how good the film is). As the system behaves stochastically and is therefore lacking repeatability, there are large hurdles to commercial deployment. Mobile media and new technologies have the potential to change the threshold for collective action due to swarm intelligence (Rheingold: 2002, P175).

The location of transmission infrastructure for wireless communication networks is an important engineering problem involving competing objectives. A minimal selection of locations (or sites) are required subject to providing adequate area coverage for users. A very different, ant-inspired swarm intelligence algorithm, stochastic diffusion search (SDS), has been successfully used to provide a general model for this problem, related to circle packing and set covering. It has been shown that the SDS can be applied to identify suitable solutions even for large problem instances.

Airlines have also used ant-based routing in assigning aircraft arrivals to airport gates. At Southwest Airlines a software program uses swarm theory, or swarm intelligence—the idea that a colony of ants works better than one alone. Each pilot acts like an ant searching for the best airport gate. "The pilot learns from his experience what's the best for him, and it turns out that that's the best solution for the airline," Douglas A. Lawson explains. As a result, the "colony" of pilots always go to gates they can arrive at and depart from quickly. The program can even alert a pilot of plane back-ups before they happen. "We can anticipate that it's going to happen, so we'll have a gate available," Lawson says.

Crowd simulation

Artists are using swarm technology as a means of creating complex interactive systems or simulating crowds.

Instances

The Lord of the Rings film trilogy made use of similar technology, known as Massive (software), during battle scenes. Swarm technology is particularly attractive because it is cheap, robust, and simple.

Stanley and Stella in: Breaking the Ice was the first movie to make use of swarm technology for rendering, realistically depicting the movements of groups of fish and birds using the Boids system.

Tim Burton's Batman Returns also made use of swarm technology for showing the movements of a group of bats.

Airlines have used swarm theory to simulate passengers boarding a plane. Southwest Airlines researcher Douglas A. Lawson used an ant-based computer simulation employing only six interaction rules to evaluate boarding times using various boarding methods.(Miller, 2010, xii-xviii).

Human swarming

Networks of distributed users can be organized into "human swarms" through the implementation of real-time closed-loop control systems. Developed by Louis Rosenberg in 2015, human swarming, also called artificial swarm intelligence, allows the collective intelligence of interconnected groups of people online to be harnessed. The collective intelligence of the group often exceeds the abilities of any one member of the group.

Stanford University School of Medicine published in 2018 a study showing that groups of human doctors, when connected together by real-time swarming algorithms, could diagnose medical conditions with substantially higher accuracy than individual doctors or groups of doctors working together using traditional crowd-sourcing methods. In one such study, swarms of human radiologists connected together were tasked with diagnosing chest x-rays and demonstrated a 33% reduction in diagnostic errors as compared to the traditional human methods, and a 22% improvement over traditional machine-learning.

The University of California San Francisco (UCSF) School of Medicine released a preprint in 2021 about the diagnosis of MRI images by small groups of collaborating doctors. The study showed a 23% increase in diagnostic accuracy when using Artificial Swarm Intelligence (ASI) technology compared to majority voting.

Swarm grammars

Swarm grammars are swarms of stochastic grammars that can be evolved to describe complex properties such as found in art and architecture. These grammars interact as agents behaving according to rules of swarm intelligence. Such behavior can also suggest deep learning algorithms, in particular when mapping of such swarms to neural circuits is considered.

Swarmic art

In a series of works, al-Rifaie et al. have successfully used two swarm intelligence algorithms—one mimicking the behaviour of one species of ants (Leptothorax acervorum) foraging (stochastic diffusion search, SDS) and the other algorithm mimicking the behaviour of birds flocking (particle swarm optimization, PSO)—to describe a novel integration strategy exploiting the local search properties of the PSO with global SDS behaviour. The resulting hybrid algorithm is used to sketch novel drawings of an input image, exploiting an artistic tension between the local behaviour of the 'birds flocking'—as they seek to follow the input sketch—and the global behaviour of the "ants foraging"—as they seek to encourage the flock to explore novel regions of the canvas. The "creativity" of this hybrid swarm system has been analysed under the philosophical light of the "rhizome" in the context of Deleuze's "Orchid and Wasp" metaphor.

A more recent work of al-Rifaie et al., "Swarmic Sketches and Attention Mechanism", introduces a novel approach deploying the mechanism of 'attention' by adapting SDS to selectively attend to detailed areas of a digital canvas. Once the attention of the swarm is drawn to a certain line within the canvas, the capability of PSO is used to produce a 'swarmic sketch' of the attended line. The swarms move throughout the digital canvas in an attempt to satisfy their dynamic roles—attention to areas with more details—associated with them via their fitness function. Having associated the rendering process with the concepts of attention, the performance of the participating swarms creates a unique, non-identical sketch each time the 'artist' swarms embark on interpreting the input line drawings. In other works, while PSO is responsible for the sketching process, SDS controls the attention of the swarm.

In a similar work, "Swarmic Paintings and Colour Attention", non-photorealistic images are produced using SDS algorithm which, in the context of this work, is responsible for colour attention.

The "computational creativity" of the above-mentioned systems are discussed in through the two prerequisites of creativity (i.e. freedom and constraints) within the swarm intelligence's two infamous phases of exploration and exploitation.

Michael Theodore and Nikolaus Correll use swarm intelligent art installation to explore what it takes to have engineered systems to appear lifelike.

Psychedelic experience

From Wikipedia, the free encyclopedia

https://en.wikipedia.org/wiki/Psychedelic_experience

A psychedelic experience (known colloquially as a trip) is a temporary altered state of consciousness induced by the consumption of a psychedelic substance (most commonly LSD, mescaline, psilocybin mushrooms, or DMT). For example, an acid trip is a psychedelic experience brought on by the use of LSD, while a mushroom trip is a psychedelic experience brought on by the use of psilocybin. Psychedelic experiences feature alterations in normal perception such as visual distortions and a subjective loss of self-identity, sometimes interpreted as mystical experiences . Psychedelic experiences lack predictability, as they can range from being highly pleasurable (known as a good trip) to frightening (known as a bad trip). The outcome of a psychedelic experience is heavily influenced by the person's mood, personality, expectations, and environment (also known as set and setting).

Researchers have interpreted psychedelic experiences in light of a range of scientific theories, including model psychosis theory, filtration theory, psychoanalytic theory, entropic brain theory, integrated information theory, and predictive processing. Psychedelic experiences are also induced and interpreted in religious and spiritual contexts.

Along with psilocybin's unique effect on the state of mind, psilocybin has been subject to the idea of being used for therapeutic treatments. This rapidly developing field of psilocybin-assisted therapy has produced promising results in studies targeting mental disorders like depression, post-traumatic stress disorder (PTSD), and obsessive-compulsive disorder (OCD).

Etymology

The term psychedelic was coined by the psychiatrist Humphrey Osmond during written correspondence with author Aldous Huxley and presented to the New York Academy of Sciences by Osmond in 1957. It is derived from the Greek words ψυχή, psychḗ, 'soul, mind' and δηλείν, dēleín, 'to manifest' thus meaning "mind manifesting," the implication being that psychedelics can develop untapped potentials of the human mind. The term trip was first coined by US Army scientists during the 1950s when they were experimenting with LSD.

Phenomenology

Despite several attempts that have been made, starting in the 19th and 20th centuries, to define common phenomenological structures of the effects produced by classic psychedelics, a universally accepted taxonomy does not yet exist.

Visual alteration

A prominent element of psychedelic experiences is visual alteration. Psychedelic visual alteration often includes spontaneous formation of complex flowing geometric visual patterning in the visual field. When the eyes are open, the visual alteration is overlaid onto the objects and spaces in the physical environment; when the eyes are closed the visual alteration is seen in the "inner world" behind the eyelids. These visual effects increase in complexity with higher dosages, and also when the eyes are closed. The visual alteration does not normally constitute hallucinations, because the person undergoing the experience can still distinguish between real and imagined visual phenomena, though in some cases, true hallucinations are present. More rarely, psychedelic experiences can include complex hallucinations of objects, animals, people, or even whole landscapes. Visual alterations also include other effects such as afterimages, shifting of color hues, and pareidolia. The appearance of colors and light are usually enhanced.

The visuals of psychedelics have been reproduced in video and image form using artificial intelligence.

Mystical experiences

Further information: Ego death

A number of studies by Roland R. Griffiths and other researchers have concluded that high doses of psilocybin and other classic psychedelics trigger mystical experiences in most research participants. Mystical experiences have been measured by a number of psychometric scales, including the Hood Mysticism Scale, the Spiritual Transcendence Scale, and the Mystical Experience Questionnaire. The revised version of the Mystical Experience Questionnaire, for example, asks participants about four dimensions of their experience, namely the "mystical" quality, positive mood such as the experience of amazement, the loss of the usual sense of time and space, and the sense that the experience cannot be adequately conveyed through words. The questions on the "mystical" quality in turn probe multiple aspects: the sense of "pure" being, the sense of unity with one's surroundings, the sense that what one experienced was real, and the sense of sacredness. Some researchers have questioned the interpretation of the results from these studies and whether the framework and terminology of mysticism are appropriate in a scientific context, while other researchers have responded to those criticisms and argued that descriptions of mystical experiences are compatible with a scientific worldview.

A group of researchers concluded in a 2011 study that psilocybin "occasions personally and spiritually significant mystical experiences that predict long-term changes in behaviors, attitudes and values".

Some research has found similarities between psychedelic experiences and non-ordinary forms of consciousness experienced in meditation and near-death experiences. The phenomenon of ego dissolution is often described as a key feature of the psychedelic experience.

Individuals who have psychedelic experiences often describe what they experienced as "more real" than ordinary experience. For example, the psychologist Benny Shanon, after observing ayahuasca trips, referred to "the assessment, very common with ayahuasca, that what is seen and thought during the course of intoxication defines the real, whereas the world that is ordinarily perceived is actually an illusion." Similarly, the psychiatrist Stanislav Grof described the LSD experience as "complex revelatory insights into the nature of existence… typically accompanied by a sense of certainty that this knowledge is ultimately more relevant and 'real' than the perceptions and beliefs we share in everyday life."

Bad trips

Main article: Bad trip

A "bad trip" is a highly unpleasant psychedelic experience. A bad trip on psilocybin, for instance, often features intense anxiety, confusion, agitation, or even psychotic episodes. Bad trips can be connected to the anxious ego-dissolution (AED) dimension of the APZ questionnaire used in research on psychedelic experiences. As of 2011, exact data on the frequency of bad trips are not available. Some research suggests that the risk of a bad trip on psilocybin is higher when multiple drugs are used, when the user has a history of certain mental illnesses, and when the user is not supervised by a sober person.

In clinical research settings, precautions including the screening and preparation of participants, the training of the session monitors who will be present during the experience, and the selection of appropriate physical setting can minimize the likelihood of psychological distress. Researchers have suggested that the presence of professional "trip sitters" (i.e., session monitors) may significantly reduce the negative experiences associated with a bad trip. In most cases in which anxiety arises during a supervised psychedelic experience, reassurance from the session monitor is adequate to resolve it; however, if distress becomes intense it can be treated pharmacologically, for example with the benzodiazepine diazepam.Research shows that preparing for the psychedelic experience, as well as the set and setting of the individual and environment they will be in, can help mitigate "bad trips''. Harvard Psychologist Timothy Leary has said that "set" and "setting" are important to the experience. Set refers to the participants' internal state – their mental, emotional and physical state, as well as their intentions for the experience (whether they want to solve a complex problem, discover the underlying secrets of the universe, or heal from a past trauma) – the better these preliminary conditions, the better the experience usually goes. Setting refers to the environment the experience will take place in. Leary and others have found that, due to the highly suggestible nature of the psychedelic experience, the environment the participant is in plays a critical role. For example, a warmly decorated room with a comfortable couch, nice music and an overall welcoming atmosphere will have a much more positive effect than a cold stainless steel and concrete reinforced hospital room. Taking these necessary precautions before a psychedelic experience, along with the presence of trained professionals, have been shown to significantly reduce an overall negative experience.

The psychiatrist Stanislav Grof wrote that unpleasant psychedelic experiences are not necessarily unhealthy or undesirable, arguing that they may have potential for psychological healing and lead to breakthrough and resolution of unresolved psychic issues. Drawing on narrative theory, the authors of a 2021 study of 50 users of psychedelics found that many described bad trips as having been sources of insight or even turning points in life.

Scientific models

Link R. Swanson divides scientific frameworks for understanding psychedelic experiences into two waves. In the first wave, encompassing nineteenth- and twentieth-century frameworks, he includes model psychosis theory (the psychotomimetic paradigm), filtration theory, and psychoanalytic theory. In the second wave of theories, encompassing twenty-first-century frameworks, Swanson includes entropic brain theory, integrated information theory, and predictive processing.

Model psychosis theory

Researchers studying mescaline in the early twentieth century and LSD in the mid-twentieth century took interest in these drugs as producing a temporary "model psychosis" that could assist researchers and medical students in understanding the experiences of patients with schizophrenia and other psychotic disorders.

It was popular to compare between experiences of psychedelics and psychosis in the mid-20th century. The scales used in psychosis and psychedelic research, in the late-20th and 21st century, are more different. Despite the many similarities that were observed between experiences of psychedelics and psychosis in the past, contemporary psychosis and psychedelic research highlight some features more than others (since they have different goals and assumptions), such as mysticism, connectedness, awe, peace, ego dissolution, hallucinations, suspiciousness, disorganization, hostility, grandiosity, and withdrawal.

Filtration theory

Aldous Huxley and Humphrey Osmond applied the pre-existing ideas of filtration theory, which held that the brain filters what enters into consciousness, to explain psychedelic experiences (and it is from this paradigm that the term psychedelic is derived). Huxley believed that the brain was filtering reality itself and that psychedelics granted conscious access to "Mind at Large," whereas Osmond believed that the brain was filtering aspects of the mind out of consciousness. Swanson writes that Osmond's view seems "less radical, more compatible with materialist science, and less epistemically and ontologically committed" than Huxley's.

Psychoanalytic theory

Psychoanalytic theory was the predominant interpretive framework in mid-twentieth-century psychedelic-assisted psychotherapy. For instance, Czech psychiatrist Stanislav Grof characterised psychedelic experiencing as "non-specific amplification of unconscious mental processes", and he analysed the phenomenology of the LSD experience (particularly the experience of what he termed psychospiritual death and rebirth) in terms of Otto Rank's theory of the unresolved memory of the primal birth trauma.

Entropic brain theory

Entropic brain theory is a theory of consciousness proposed in 2014 by neuroscientist Robin Carhart-Harris and colleagues that was inspired by research on psychedelic drugs. The theory suggests that the entropy of brain activity within certain limits indexes the richness of conscious states, particularly under the influence of psychedelics. This theory posits that elevated brain entropy correlates with heightened informational richness, suggesting that psychedelics increase brain criticality, making it more sensitive to internal and external perturbations. This enhanced state of brain activity is proposed to influence susceptibility to environmental factors ("set" and "setting") and potentially offer new insights for treating psychiatric and neurological disorders, including depression and disorders of consciousness.

Integrated information theory

Integrated information theory is a theory of consciousness proposing to explain all forms of consciousness, and has been applied specifically to psychedelic experiences by Andrew Gallimore.

Predictive processing

Sarit Pink-Hashkes and colleagues have applied the predictive processing paradigm in neuroscience to psychedelic experiences in order to formalize the idea of the entropic brain.

In religious and spiritual contexts

Further information: Entheogen

Alan Watts likened psychedelic experiencing to the transformations of consciousness that are undertaken in Taoism and Zen, which he says is, "more like the correction of faulty perception or the curing of a disease… not an acquisitive process of learning more and more facts or greater and greater skills, but rather an unlearning of wrong habits and opinions." Watts further described the LSD experience as, "revelations of the secret workings of the brain, of the associative and patterning processes, the ordering systems which carry out all our sensing and thinking."

According to Luis Luna, psychedelic experiences have a distinctly gnosis-like quality; it is a learning experience that elevates consciousness and makes a profound contribution to personal development. For this reason, the plant sources of some psychedelic drugs such as ayahuasca and mescaline-containing cacti are sometimes referred to as "plant teachers" by those using those drugs.

Furthermore, psychedelic drugs have a history of religious use across the world that extends back for hundreds or perhaps thousands of years. They are often called entheogens because of the kinds of experiences they can induce, however various entheogens happen to also be hypnotics (muscimol mushrooms), deliriants (jimsonweed) or atypical/quasi-psychedelics like cannabis. Some small contemporary religious movements base their religious activities and beliefs around psychedelic experiences, such as Santo Daime and the Native American Church.

Psilocybin-assisted therapy

Depression

Studies on psilocybin-assisted therapy have found participants experience reduced depressive symptoms afterwards, as well as reduced anxiety symptoms. Studies have also found that reductions in symptoms continued long afterwards, suggesting psilocybin could potentially be effective as a long-term treatment.

Post-traumatic stress disorder

Individuals who suffer from post-traumatic stress disorder (PTSD) may also benefit from psilocybin-assisted therapy. Based on studies so far, MDMA-assisted therapy appears to be effective for reducing symptoms of PTSD, leading a group of researchers to argue that psilocybin-assisted therapy may also be effective in PTSD and call for a study on the topic.

Obsessive-compulsive disorder

In a study that reviewed a variety of drugs to determine if it had an impact on symptoms of OCD, psilocybin was also tested and determined effective in reducing symptoms. This reduction in symptoms applied to all individuals who participated in the study, proving psilocybin to be very reliable along with efficiency in reducing OCD symptoms.