Inspiration for optimization from social insect behaviour

Bonabeau, Eric; Dorigo, Marco; Théraulaz, Guy

doi:10.1038/35017500

Cited by 800 publications

(382 citation statements)

References 25 publications

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“…However, the MCSDS algorithm (as deployed by Aunt Hillary) is unable to reliably play this way; to do so would require the anticipation of potential next moves from the opponent (see figure 3). To improve the tactical play, a metapopulation [23] of ants is deployed to enable a Monte-Carlo informed SDS to tactically explore the game tree 6 : Stochastic Diffusion Search applied to Trees (SDST).…”

Section: Discussionmentioning

confidence: 99%

“…In the study of interaction in social insects, two key elements are the individuals and the environment, which results in two modes of interaction: the first defines the way in which individuals interact with each other and the second defines the interaction of individuals with the environment [6]. Interaction between individual agents is typically carried out via agent recruitment processes and it has been demonstrated that various recruitment strategies are deployed by ants [19] and honey bees [15,32].…”

Section: Swarm Intelligencementioning

confidence: 99%

“…This paper introduces the central Monte Carlo theme to 6 A 'game tree' is a directed graph whose nodes are positions in a game and whose edges are moves. The complete game tree for a game is the game tree starting at the initial position and containing all possible moves from each position; a n-ply game tree describes all possible move/counter-move combinations to a depth of n moves.…”

Section: Monte-carlo Tree Search (Mcts)mentioning

confidence: 99%

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HeX and the Single Anthill: Playing Games with Aunt Hillary

Bishop

Nasuto

Tanay

et al. 2016

Fundamental Issues of Artificial Intelligence

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In a reflective and richly entertaining piece from 1979, Doug Hofstadter playfully imagined a conversation between 'Achilles' and an anthill (the eponymous 'Aunt Hillary'), in which he famously explored many ideas and themes related to cognition and consciousness. For Hofstadter, the anthill is able to carry on a conversation because the ants that compose it play roughly the same role that neurons play in human languaging; unfortunately, Hofstadter's work is notably short on detail suggesting how this magic might be achieved 1 . Conversely in this paper -finally reifying Hofstadter's imagination -we demonstrate how populations of simple ant-like creatures can be organised to solve complex problems; problems that involve the use of forward planning and strategy. Specifically we will demonstrate that populations of such creatures can be configured to play a strategically strongthough tactically weak -game of HeX (a complex strategic game). We subsequently demonstrate how tactical play can be improved by introducing a form of forward planning instantiated via multiple populations of agents; a technique that can be compared to the dynamics of interacting populations of social insects via the concept of meta-population. In this way although, pace Hofstadter, we do not establish that a meta-population of ants could actually hold a conversation with Achilles, we do successfully introduce Aunt Hillary to the complex, seductive charms of HeX.J. Mark Bishop Goldsmiths, University of London, UK, e-mail: m.bishop@gold.ac.uk 1 As Drew McDermott writes in the Cambridge Handbook of Consciousness [24], it is as if Hofstadter "wants to invent a new, playful style of argumentation, in which concepts are broken up and tossed together into so many configurations that the original question one might have asked get shunted aside".

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Section: Discussionmentioning

confidence: 99%

Section: Swarm Intelligencementioning

confidence: 99%

Section: Monte-carlo Tree Search (Mcts)mentioning

confidence: 99%

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HeX and the Single Anthill: Playing Games with Aunt Hillary

Bishop

Nasuto

Tanay

et al. 2016

Fundamental Issues of Artificial Intelligence

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“…However, there are many other methods that have already been, or could be, applied to such problems: examples include antbased computation [73,74], evolutionary Kohonen networks [27*], fuzzy clustering [23], support vector machines [75] and Bayesian learning [66]. We believe that methods such as these will prove invaluable in the analysis of the huge volumes of data that characterise modern pharmaceutical research, particularly when used in combination [76**.77**].…”

Section: Discussionmentioning

confidence: 99%

Visual and computational analysis of structure–activity relationships in high-throughput screening data

Gedeck

Willett

2001

Current Opinion in Chemical Biology

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Published paper Summary of Recent AdvancesNovel analytic methods are required to assimilate the large volumes of structural and bioassay data generated by combinatorial chemistry and high-throughput screening programmes in the pharmaceutical and agrochemical industries. This paper reviews recent work in visualisation and data mining that can be used to develop structure-activity relationships from such chemical/biological datasets.

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“…The quantity of pheromone deposited, which may depend on the quantity and quality of the food, will guide other ants to the food source. Thus a shorter path tends to have a higher pheromone density, making it more likely to be chosen by other ants (Bonabeau et al 2000). This shortest path represents the global optimal solution and all the possible paths represent the feasible region of the problem.…”

Section: Using Aco To Solve Groundwater Management Modelmentioning

confidence: 99%

Swarm intelligence for groundwater management optimization

Sedki

Ouazar

2010

Journal of Hydroinformatics

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This paper presents some simulation-optimization models for groundwater resources management. These models couple two of the most successful global optimization techniques inspired by swarm intelligence, namely particle swarm optimization (PSO) and ant colony optimization (ACO), with one of the most commonly used groundwater flow simulation code, MODFLOW. The coupled simulation-optimization models are formulated and applied to three different groundwater management problems: (i) maximization of total pumping problem,(ii) minimization of total pumping to contain contaminated water within a capture zone and (iii) minimization of the pumping cost to satisfy the given demand for multiple management periods. The results of PSO-and ACO-based models are compared with those produced by other methods previously presented in the literature for the three case studies considered. It is found that PSO and ACO are promising methods for solving groundwater management problems, as is their ability to find optimal or near-optimal solutions.

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Inspiration for optimization from social insect behaviour

Cited by 800 publications

References 25 publications

HeX and the Single Anthill: Playing Games with Aunt Hillary

HeX and the Single Anthill: Playing Games with Aunt Hillary

Visual and computational analysis of structure–activity relationships in high-throughput screening data

Swarm intelligence for groundwater management optimization

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