Comparative Reproduction Schemes for Evolving Gathering Collectives A.E. Eiben, G.S. Nitschke, M.C. Schut Computational Intelligence Group Department of Computer Science, Faculty of Sciences De Boelelaan 1081a, 1081 HV Amsterdam The Netherlands

Introduction Research theme: Emergent Collective Intelligence Investigating artificial evolution for adaptability at local level, and desired emergent behaviors at global level Comparing agent reproduction schemes under two types of evolvable controllers: heuristic and neural-net Results from a collective gathering simulation

Background Emergent behavior in collective gathering: Nolfi et al. (2003): Cooperative transport by s-bots Drogoul et al. (1995): Emergent functionality, division of labor in a simulated ant colony gathering task Perez-Uribe et al. (2003):Emergent cooperative behavior for gathering by artificial ants, based on colony type (heterogeneous vs. homogenous)

Experimental Setup JAWAS simulator: simulating collective gathering - potentially thousands of agents (swarm scape) Initially 1000 agents, 3 resource types (different values and cooperation needed to gather) Agent goal: to gather the highest value of resources possible during lifetime Cooperation needed for ‘good’ solutions – gathering of highest value of resources

Task domain: Minesweeping Capacity Extraction Transport Fitness Threshold Cost Cost Reward Mine type A Mine type B Mine type C Fitness rewards given to agents for successful gathering (delivery to the home area). Collective evaluation: Total value gathered – taken at end of Simulation and as average of 100 runs

Comparative Agent Reproduction Schemes Temporal Dimension SREL : Single Reproduction at End of Lifetime MRDL: Multiple Reproduction during an Agents Lifetime Spatial Dimension Locally restricted: Reproduction only with agents in adjacent cells Panmictic: Reproduction with agents anywhere in the environment

Agent Controllers: Heuristic Controller

Agent Controllers: Neural Network Controller

Evolutionary setup For both heuristic and NN : –Gathering and transport parameter values evolved Heuristic controller static throughout evolutionary process Neural network controller dynamic over evolutionary process – i.e. NN weights evolved NN controllers evolved under a neuro-evolution process

Experimental Results

Experimental Results: Evolution Control

Conclusions SREL/Local – the most effective scheme – under given behavior evaluation - validated under two approaches Not the case under MRDL – where agents may not be given sufficient time to adapt to their task before reproducing Fitness saved by agents until end of lifetime – successful agents pass on fitness – especially effective under NE – where recombined mutated weights are passed on