Stigmergy - Advantages. Autonomous Agent. Real Ants. Stigmergy. Self-Organization - Characteristics. Self-Organization Alternatives

Transcription

1 Last time Cellular automata One-dimensional Wolfram s classification Langton s lambda parameter Two-dimensional Conway s Game of Life Pattern formation in slime molds Dictyostelium discoideum Modeling of pattern Self-Organization Pattern A particular, organized arrangement of objects in space or time Interactions Based on local information only - no global information Physical laws Genetically controlled properties of the components 1 4 Outline for today Self-Organization Autonomous Agents Real Ants Virtual Termites Virtual Ants Ant Algorithms Self-Organization - Ingredients Positive feedback Activity amplification Negative feedback Activity balancing Amplification of random fluctuations Multiple interactions 2 5 Self-Organization Self-organization is a process in which pattern at the global level of a system emerges solely from numerous interactions among the lower-level components of the system. Moreover, the rules specifying interactions among the system s components are executed using only local information, without reference to the global pattern. Camazine et al, p. 8 3 Self-Organization - Information Signals Stimuli shaped by natural selection specifically to convey information Cues Stimuli that convey information only incidentally Gathered from one s neighbors Stimuli-response, simple behavioral rules of thumb Gathered from work in progress Stigmergy Random fluctuation and chance heterogeneities 6 1

2 Self-Organization - Characteristics Dynamic systems Exhit emergent properties Attractors Multistability Bifurcations Parameter tuning Environmental factors Adaptive systems Different patterns may result from the same mechanism Simple rules, complex patterns Stigmergy - Advantages Permite simpler agents Decrease direct communication between agents Incremental improvment Flexible, since when environment changes, agents respond appropriately 7 10 Self-Organization Alternatives Autonomous Agent Central leader Need effective communication and cognitive abilities Blueprints Most be stored Recipes Hinders flexibility Templates Must be avaiable a unit that interacts with its environment (which probably consists of other agents) but acts independently from all other agents in that it does not take commands from some seen or unseen leader, nor does an agent have some idea of a global plan that it should be following. - Flake, p Stigmergy Real Ants A recursive control system Effective for coordination in space and time A sequence of qualitatively different stimulus-response behaviors Two types: Qualitative stigmergy Quantitative stigmergy Imagine if artificial systems could do the things ants can do? Why ants? Amazonas: 30% of biomass is ants/termites Amazonas: dry weight of social insects is four times that of other land animals Earth: ~10% of total biomass (like humans)

3 Army Ants s in colony Create temporary bivouacs Act like unified entity Harvester Ants Find shortest path to food Prioritize food sources based on distance and ease of access (Picture from The Texas A&M University System) (Pictures from AntColony.org) Fungus-Growing Ants "A Leaf Cutter Colony can strip the tallest of trees in a single day. Equivalent consumption of a full grown cow in the same time!" Cultivate fungi underground Fertilize with compost from chewed leaves (Pictures from AntColony.org) 14 Adaptive Path Optimization 17 Fungus Cultivator Nest Virtual Termites The assigment Why does the number of piles decrease? How to improve the performance with two type of termites and two type of chips? How does destroyers affect the system? (Picture from AntColony.org)

4 Langton s Virtual Ants Virtual Ants - Conclusion Grid with white or black squares Virtual ants can face N, S, E, W Behavioral rule: Take a step forward if on a white square then paint it black and turn 90º right if on a black square then paint it white and turn 90º left Even simple, reversible local behavior can lead to complex global behavior Such complex behavior may create structures as well as apparently random behavior Virtual Ants - Example Ant Algorithms Ant colony optimization (ACO) Developed in 1991 by Dorigo (PhD dissertation) in collaboration with Colorni and Maniezzo Virtual Ants Time Reversibility Summary Virtual ants are time-reversible But, time-reversibility does not imply global simplicity Even a single virtual ant interacts with its own prior history Demonstration Self-Organization Autonomous Agents Real Ants Virtual Termites Virtual Ants Ant Algorithms

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