In philosophy, four-dimensionalism (also known as the doctrine of temporal parts) is the ontological
position that an object's persistence through time is like its
extension through space. Thus, an object that exists in time has
temporal parts in the various subregions of the total region of time it
occupies, just like an object that exists in a region of space has at
least one part in every subregion of that space.
Four-dimensionalists typically argue for treating time as analogous to space, usually leading them to endorse the doctrine of eternalism. This is a philosophical approach to the ontological nature of time, according to which all points in time are equally "real", as opposed to the presentist idea that only the present is real. As some eternalists argue by analogy, just as all spatially distant
objects and events are as real as those close to us, temporally distant
objects and events are as real as those currently present to us.
Perdurantism—or perdurance theory—is a closely related philosophical theory of persistence and identity, according to which an individual has distinct temporal parts throughout
its existence, and the persisting object is the sum or set of all of
its temporal parts. This sum or set is colloquially referred to as a
"space-time worm", which has earned the perdurantist view the moniker of
"the worm view". While all perdurantists are plausibly considered four dimensionalists,
at least one variety of four dimensionalism does not count as
perdurantist in nature. This variety, known as exdurantism or the
"stage view", is closely akin to the perdurantist position. They also
countenance a view of persisting objects that have temporal parts that
succeed one another through time. However, instead of identifying the
persisting object as the entire set or sum of its temporal parts, the
exdurantist argues that any object under discussion is a single stage
(time-slice, temporal part, etc.), and that the other stages or parts
that comprise the persisting object are related to that part by a
"temporal counterpart" relation.
Though they have often been conflated, eternalism is a theory of
what time is like and what times exist, while perdurantism is a theory
about persisting objects and their identity conditions over time.
Eternalism and perdurantism tend to be discussed together because many
philosophers argue for a combination of eternalism and perdurantism.
Sider (1997) uses the term four-dimensionalism to refer to perdurantism, but Michael Rea uses the term "four-dimensionalism" to mean the view that presentism is false as opposed to "perdurantism", the view that endurantism is false and persisting objects have temporal parts.
Four-dimensionalism about material objects
Four-dimensionalism
is a name for different positions. One of these uses
four-dimensionalism as a position of material objects with respect to
dimensions. Four-dimensionalism is the view that in addition to spatial
parts, objects have temporal parts.
According to this view, four-dimensionalism cannot be used as a
synonym for perdurantism. Perdurantists have to hold a four-dimensional
view of material objects: it is impossible that perdurantists, who
believe that objects persist by having different temporal parts at
different times, do not believe in temporal parts. However, the reverse
is not true. Four-dimensionalism is compatible with either perdurantism
or exdurantism.
J.M.E. McTaggart in The Unreality of Time
identified two descriptions of time, which he called the A-series and
the B-series. The A-series identifies positions in time as past,
present, or future, and thus assumes that the "present" has some
objective reality, as in both presentism and the growing block universe. The B-series defines a given event as earlier or later than another
event, but does not assume an objective present, as in
four-dimensionalism. Much of the contemporary literature in the metaphysics of time has been taken to spring forth from this distinction, and thus takes McTaggart's work as its starting point.
Contrast with three-dimensionalism
Unlike the four dimensionalist, the three dimensionalist considers time to be a unique dimension that is not analogous to the three spatial dimensions: length, width and height.
Whereas the four dimensionalist proposes that objects are extended
across time, the three dimensionalist adheres to the belief that all
objects are wholly present at any moment at which they exist. While the
three dimensionalist agrees that the parts of an object can be
differentiated based on their spatial dimensions, they do not believe an
object can be differentiated into temporal parts across time. For
example, in the three dimensionalist account, "Descartes in 1635" is the
same object as "Descartes in 1620", and both are identical to
Descartes, himself. However, the four dimensionalist considers these to
be distinct temporal parts.
Prominent arguments in favor of four-dimensionalism
Several lines of argumentation have been advanced in favor of four-dimensionalism:
Firstly, four-dimensional accounts of time are argued to better
explain paradoxes of change over time (often referred to as the paradox
of the Ship of Theseus) than three-dimensional theories. A contemporary account of this paradox is introduced in Ney (2014), but the original problem has its roots in Greek antiquity. A typical
Ship of Theseus paradox involves taking some changeable object with
multiple material parts, for example a ship, then sequentially removing
and replacing its parts until none of the original components are left.
At each stage of the replacement, the ship is presumably identical with
the original, since the replacement of a single part need not destroy
the ship and create an entirely new one. But, it is also plausible that
an object with none of the same material parts as another is not
identical with the original object. So, how can an object survive the
replacement of any of its parts, and in fact all of its parts? The
four-dimensionalist can argue that the persisting object is a single
space-time worm which has all the replacement stages as temporal parts,
or in the case of the stage view that each succeeding stage bears a
temporal counterpart relation to the original stage under discussion.
Secondly, problems of temporary intrinsics are argued to be best
explained by four-dimensional views of time that involve temporal parts.
As presented by David Lewis, the problem of temporary intrinsics involves properties of an object
that are both had by that object regardless of how anything else in the
world is (and thus intrinsic), and subject to change over time (thus
temporary). Shape is argued to be one such property. So, if an object is
capable of having a particular shape, and also changing its shape at
another time, there must be some way for the same object to be, say,
both round and square. Lewis argues that separate temporal parts having
the incompatible properties best explains an object being able to change
its shape in this way, because other accounts of three-dimensional time
eliminate intrinsic properties by indexing them to times and making
them relational instead of intrinsic.
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.
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.
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 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.
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.
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.
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 translatingstructural 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.
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.
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.
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.
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.
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.
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.
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.
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.
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 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 chitinaseenzymes 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 (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
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:
How to carry out communication and interaction of agents and which communication language or protocols should be used.
How to ensure the coherency of agents.
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
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.
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:
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.
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 ACMSIGGRAPH 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 (SPP), also referred to as the Vicsek model, was introduced in 1995 by Vicseket 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 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 (ACO), introduced by Dorigo in his doctoral dissertation, is a class of optimizationalgorithms 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)
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.
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.
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.
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.
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