Swarm intelligence

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.[1]

SI 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 in natural systems of SI include ant colonies, bird flocking, 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.

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 the flocking behaviour of birds. His paper on this topic was published in 1987 in the proceedings of the ACM SIGGRAPH conference.[2] The name "boid" corresponds to a shortened version of "bird-oid object", which refers to a bird-like object.[3]

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:

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

Self-propelled particles (Vicsek et al. 1995)

Self-propelled particles (SPP), also referred to as the Vicsek model, was introduced in 1995 by Vicsek et al.[4] as a special case of the boids model introduced in 1986 by Reynolds.[5] 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.[6] SPP models predict that swarming animals share certain properties at the group level, regardless of the type of animals in the swarm.[7] 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.[8][9][10]

Metaheuristics

Evolutionary algorithms (EA), particle swarm optimization (PSO), ant colony optimization (ACO) and their variants dominate the field of nature-inspired metaheuristics.[11] 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.

Stochastic diffusion search (Bishop 1989)

Stochastic diffusion search (SDS)[12][13] is an agent-based probabilistic global search and optimization technique best suited to problems where the objective function can be decomposed into multiple independent partial-functions. Each agent maintains a hypothesis which is iteratively tested by evaluating a randomly selected partial objective function parameterised by the agent's current hypothesis. In the standard version of SDS such partial function evaluations are binary, resulting in each agent becoming active or inactive. Information on hypotheses is diffused across the population via inter-agent communication. Unlike the stigmergic communication used in ACO, in SDS agents communicate hypotheses via a one-to-one communication strategy analogous to the tandem running procedure observed in Leptothorax acervorum.[14] A positive feedback mechanism ensures that, over time, a population of agents stabilise around the global-best solution. SDS is both an efficient and robust global search and optimisation algorithm, which has been extensively mathematically described.[15][16][17] Recent work has involved merging the global search properties of SDS with other swarm intelligence algorithms.[18][19]

Ant colony optimization (Dorigo 1992)

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 better solutions.[20]

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.[21][22] 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.

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.[23] Conversely al-Rifaie and Aber have used Stochastic Diffusion Search to help locate tumours.[24][25] Swarm intelligence has also been applied for data mining.[26]

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 variations since. 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 you pay for the cinema before you know 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.[27]

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.[28]

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

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).[29]

Human swarming

Enabled by mediating software such as the UNU collective intelligence platform, networks of distributed users can be organized into "human swarms" (also referred to as "social swarms") through the implementation of real-time closed-loop control systems. As published by Rosenberg (2015), such real-time control systems enable groups of human participants to behave as a unified collective intelligence.[30] When logged into the UNU platform, for example, groups of distributed users can collectively answer questions, generate ideas, and make predictions as a singular emergent entity.[31][32] Early testing shows that human swarms can out-predict individuals across a variety of real-world projections.[33]

Swarm grammars

Swarm grammars are swarms of stochastic grammars that can be evolved to describe complex properties such as found in art and architecture.[34] 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.[35]

Swarmic art

In a series of works al-Rifaie et al.[36] 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.[37]

In a more recent work of al-Rifaie et al., "Swarmic Sketches and Attention Mechanism",[38] 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 to 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",[39] 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[36][40][41][42] 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[43] Notable work include Swarm Wall (2012) and endo-exo (2014).

Notable researchers

See also

References

  1. ↑ Beni, G., Wang, J. Swarm Intelligence in Cellular Robotic Systems, Proceed. NATO Advanced Workshop on Robots and Biological Systems, Tuscany, Italy, June 26–30 (1989)
  2. ↑ Reynolds, Craig (1987). "Flocks, herds and schools: A distributed behavioral model.". SIGGRAPH '87: Proceedings of the 14th annual conference on Computer graphics and interactive techniques. Association for Computing Machinery: 25–34. doi:10.1145/37401.37406. ISBN 0-89791-227-6.
  3. ↑ Banks, Alec; Vincent, Jonathan; Anyakoha, Chukwudi (July 2007). "A review of particle swarm optimization. Part I: background and development". Natural Computing. doi:10.1007/s11047-007-9049-5.
  4. ↑ Vicsek, T.; Czirok, A.; Ben-Jacob, E.;; Cohen, I.; Shochet, O. (1995). "Novel type of phase transition in a system of self-driven particles". Physical Review Letters. 75: 1226–1229. arXiv:cond-mat/0611743Freely accessible. Bibcode:1995PhRvL..75.1226V. doi:10.1103/PhysRevLett.75.1226. PMID 10060237.
  5. ↑ Reynolds, C. W. (1987). "Flocks, herds and schools: A distributed behavioral model". Computer Graphics. 21 (4): 25–34. CiteSeerX 10.1.1.103.7187Freely accessible. doi:10.1145/37401.37406.
  6. ↑ Czirók, A.; Vicsek, T. (2006). "Collective behavior of interacting self-propelled particles". Physica A. 281: 17–29. arXiv:cond-mat/0611742Freely accessible. Bibcode:2000PhyA..281...17C. doi:10.1016/S0378-4371(00)00013-3.
  7. ↑ Buhl, J.; Sumpter, D.J.T.; Couzin, D.; Hale, J.J.; Despland, E.; Miller, E.R.; Simpson, S.J.; et al. (2006). "From disorder to order in marching locusts" (PDF). Science. 312 (5778): 1402–1406. Bibcode:2006Sci...312.1402B. doi:10.1126/science.1125142. PMID 16741126.
  8. ↑ Toner, J.; Tu, Y.; Ramaswamy, S. (2005). "Hydrodynamics and phases of flocks" (PDF). Annals of Physics. 318: 170–244. Bibcode:2005AnPhy.318..170T. doi:10.1016/j.aop.2005.04.011.
  9. ↑ Bertin, E.; Droz, M.; Grégoire, G. (2009). "Hydrodynamic equations for self-propelled particles: microscopic derivation and stability analysis". J. Phys. A. 42 (44): 445001. arXiv:0907.4688Freely accessible. Bibcode:2009JPhA...42R5001B. doi:10.1088/1751-8113/42/44/445001.
  10. ↑ Li, Y.X.; Lukeman, R.; Edelstein-Keshet, L.; et al. (2007). "Minimal mechanisms for school formation in self-propelled particles" (PDF). Physica D: Nonlinear Phenomena. 237 (5): 699–720. Bibcode:2008PhyD..237..699L. doi:10.1016/j.physd.2007.10.009.
  11. ↑ Lones, Michael A. (2014). "Metaheuristics in Nature-Inspired Algorithms" (PDF). GECCO '14. doi:10.1145/2598394.2609841.
  12. ↑ Bishop, J.M., Stochastic Searching Networks, Proc. 1st IEE Int. Conf. on Artificial Neural Networks, pp. 329-331, London, UK, (1989).
  13. ↑ Nasuto, S.J. & Bishop, J.M., (2008), Stabilizing swarm intelligence search via positive feedback resource allocation, In: Krasnogor, N., Nicosia, G, Pavone, M., & Pelta, D. (eds), Nature Inspired Cooperative Strategies for Optimization, Studies in Computational Intelligence, vol 129, Springer, Berlin, Heidelberg, New York, pp. 115-123.
  14. ↑ Moglich, M.; Maschwitz, U.; Holldobler, B., Tandem Calling: A New Kind of Signal in Ant Communication, Science, Volume 186, Issue 4168, pp. 1046-1047
  15. ↑ Nasuto, S.J., Bishop, J.M. & Lauria, S., Time complexity analysis of the Stochastic Diffusion Search, Proc. Neural Computation '98, pp. 260-266, Vienna, Austria, (1998).
  16. ↑ Nasuto, S.J., & Bishop, J.M., (1999), Convergence of the Stochastic Diffusion Search, Parallel Algorithms, 14:2, pp: 89-107.
  17. ↑ Myatt, D.M., Bishop, J.M., Nasuto, S.J., (2004), Minimum stable convergence criteria for Stochastic Diffusion Search, Electronics Letters, 22:40, pp. 112-113.
  18. ↑ al-Rifaie, M.M., Bishop, J.M. & Blackwell, T., An investigation into the merger of stochastic diffusion search and particle swarm optimisation, Proc. 13th Conf. Genetic and Evolutionary Computation, (GECCO), pp.37-44, (2012).
  19. ↑ al-Rifaie, Mohammad Majid, John Mark Bishop, and Tim Blackwell. "Information sharing impact of stochastic diffusion search on differential evolution algorithm." Memetic Computing 4.4 (2012): 327-338.
  20. ↑ Ant Colony Optimization by Marco Dorigo and Thomas Stützle, MIT Press, 2004. ISBN 0-262-04219-3
  21. ↑ Parsopoulos, K. E.; Vrahatis, M. N. (2002). "Recent Approaches to Global Optimization Problems Through Particle Swarm Optimization". Natural Computing. 1 (2-3): 235–306. doi:10.1023/A:1016568309421.
  22. ↑ Particle Swarm Optimization by Maurice Clerc, ISTE, ISBN 1-905209-04-5, 2006.
  23. ↑ Lewis, M. Anthony; Bekey, George A. "The Behavioral Self-Organization of Nanorobots Using Local Rules". Proceedings of the 1992 IEEE/RSJ International Conference on Intelligent Robots and Systems.
  24. ↑ al-Rifaie, M.M.; Aber, A. "Identifying metastasis in bone scans with Stochastic Diffusion Search". Proc. IEEE Information Technology in Medicine and Education, ITME. 2012: 519–523.
  25. ↑ al-Rifaie, Mohammad Majid, Ahmed Aber, and Ahmed Majid Oudah. "Utilising Stochastic Diffusion Search to identify metastasis in bone scans and microcalcifications on mammographs." In Bioinformatics and Biomedicine Workshops (BIBMW), 2012 IEEE International Conference on, pp. 280-287. IEEE, 2012.
  26. ↑ Martens, D.; Baesens, B.; Fawcett, T. (2011). "Editorial Survey: Swarm Intelligence for Data Mining". Machine Learning. 82 (1): 1–42. doi:10.1007/s10994-010-5216-5.
  27. ↑ Whitaker, R.M., Hurley, S.. An agent based approach to site selection for wireless networks. Proc ACM Symposium on Applied Computing, pp. 574–577, (2002).
  28. ↑ "Planes, Trains and Ant Hills: Computer scientists simulate activity of ants to reduce airline delays". Science Daily. April 1, 2008. Retrieved December 1, 2010.
  29. ↑ Miller, Peter (2010). The Smart Swarm: How understanding flocks, schools, and colonies can make us better at communicating, decision making, and getting things done. New York: Avery. ISBN 978-1-58333-390-7.
  30. ↑ http://sites.lsa.umich.edu/collectiveintelligence/wp-content/uploads/sites/176/2015/05/Rosenberg-CI-2015-Abstract.pdf
  31. ↑ "Human Swarms, a real-time method for collective intelligence".
  32. ↑ "Swarms of Humans Power A.I. Platform". DNews.
  33. ↑ Rosenberg, L.B., "Human swarming, a real-time method for parallel distributed intelligence," in Swarm/Human Blended Intelligence Workshop (SHBI), 2015, pp.1-7, 28-29 Sept. 2015 doi: 10.1109/SHBI.2015.7321685
  34. ↑ vonMammen, Sebastian; Jacob, Christian (2009). "The evolution of swarm grammars -- growing trees, crafting art and bottom-up design". Computational Intelligence. 4: 10–19. doi:10.1109/MCI.2009.933096.
  35. ↑ du Castel, Bertrand (15 July 2015). "Pattern Activation/Recognition Theory of Mind". Frontiers in Computational Neuroscience. 9 (90). doi:10.3389/fncom.2015.00090.
  36. 1 2 al-Rifaie, MM; Bishop, J.M.; Caines, S. (2012). "Creativity and Autonomy in Swarm Intelligence Systems". Cognitive Computing. 4 (3): 320–331. doi:10.1007/s12559-012-9130-y.
  37. ↑ Deleuze G, Guattari F, Massumi B. A thousand plateaus. Minneapolis: University of Minnesota Press; 2004.
  38. ↑ al-Rifaie, Mohammad Majid, and John Mark Bishop. "Swarmic sketches and attention mechanism". Evolutionary and Biologically Inspired Music, Sound, Art and Design. Springer Berlin Heidelberg, 2013. 85-96.
  39. ↑ al-Rifaie, Mohammad Majid, and John Mark Bishop. "Swarmic paintings and colour attention". Evolutionary and Biologically Inspired Music, Sound, Art and Design. Springer Berlin Heidelberg, 2013. 97-108.
  40. ↑ al-Rifaie, Mohammad Majid, Mark JM Bishop, and Ahmed Aber. "Creative or Not? Birds and Ants Draw with Muscle." Proceedings of AISB'11 Computing and Philosophy (2011): 23-30.
  41. ↑ al-Rifaie, Mohammad Majid, Ahmed Aber and John Mark Bishop. "Cooperation of Nature and Physiologically Inspired Mechanisms in Visualisation." Biologically-Inspired Computing for the Arts: Scientific Data through Graphics. IGI Global, 2012. 31-58. Web. 22 Aug. 2013. doi:10.4018/978-1-4666-0942-6.ch003
  42. ↑ al-Rifaie MM, Bishop M (2013) Swarm intelligence and weak artificial creativity. In: The Association for the Advancement of Artificial Intelligence (AAAI) 2013: Spring Symposium, Stanford University, Palo Alto, California, U.S.A., pp 14–19
  43. ↑ N. Correll, N. Farrow, K. Sugawara, M. Theodore (2013): The Swarm Wall: Toward Life’s Uncanny Valley. In: K. Goldberg, H. Knight, P. Salvini (Ed.): IEEE International Conference on Robotics and Automation, Workshop on Art and Robotics: Freud's Unheimlich and the Uncanny Valley.

Further reading

External links

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