Transcript ppt
Emergence in Artificial Societies Evolving Communication and Cooperation in a Sugarscape world by Pieter Buzing Plan • • • • • • • What are ‘artificial societies’? Sugarscape Our goal: communication and cooperation Our model: VUScape Setup Results Conclusions Artificial Society? • Multi agent system – 2 levels: autonomous parts, behaviour of whole – AS: more control over agents and world • Artificial life – emergent behaviour – AS: important role for individual • Agent based simulation – AS: no “problem to solve”, like optimization • Social modeling – interactions of agents; effects individual goals on population Sugarscape • Epstein & Axtell, 1996 • Torus shaped world: 50 x 50 cells • Sugar resources [0 - 4] • Agents looking for food • Evolution Sugarscape Agent “Internals” Agent Actions • • • • • • • • • • Age [0 - 100] Vision [1 - 6] Sugar Amount [0 - inf] Metabolism [1 - 4] Gender [m/f] Die Move Harvest Metabolise Reproduce Sugarscape • • • • • Reproduction rule Agent has gender: male or female Metabolism and vision are genetic! Parents: <m, v> X <M, V> Child: <m,V>, <M,v>, <m,v> or <M,V> Child inherits half of parents’ sugar Sugarscape • Agents will tend to move towards sugarhills • Agents with high vision are better off • Agents with low metabolism are better off Our Goal • Individual: – limited harvesting capabilities (maxSugarHarvest) – ability to talk and listen • Emergent behaviour: – cooperation – communication • “If cooperation is needed then talking is beneficial and communication will emerge” VUScape • Had to implement own testbed: VUScape • Model is highly based on SugarScape • The major changes: – – – – Sugar randomly distributed; multi-agent cells talkPref [0 - 1] and listenPref [0 - 1] genes Talk actions and Listen actions MaxSugarHarvest value: cooperation threshold VUScape: random landscape • • • • Instead of 2 sugar hills a random distribution 2,500 sugar units are spread across 2,500 cells 30% population drop; but still viable world (because it is harder to find food?) VUScape: limited vision range • • • • Vision range set to 1 instead of gene range [1-6] Evolution of vision is not the aim of our project local info from vision, global from communication Short-sighted agents face a tough environment VUScape: multiple agents • Cooperation scheme requires multi-agent cells • Higher population size is now possible VUScape: re-seed sugar • Agents find food, wait there until it regenerates • We need agents that are constantly searching • Explorativeness is increased by reseeding sugar after consumption VUScape: sex recovery period • To avoid possible birth explosions we implement a sex recovery period • Recovery period of 5 yields pop decrease of 11% • Flattens the age distribution Step 1: in need of help IF localAmount > maxSugarHarvest THEN inNeedOfHelp Step 2: talking IF inNeedOfHelp AND rand < talkPref THEN communicate to others on x and y axis: – cell coordinates and sugar value Step 3: listening IF rand < listenPref THEN listen to others on x and y axis Step 4: cooperating • • • • Use obtained information in movement decision. Two agents can conquer any pile! Cooperation is beneficial for both parties. Communicative agents have an advantage? Setup • Stepwise increase cooperative pressure and monitor the communicative gene values. • Experiment A: no communication – Talk and listen genes disabled • Experiment B: with communication – Talk and listen genes initiated between 0 and 1 • If communication is beneficial then an increase of talk and listen values is expected. Results: no communication Results: with communication Results: listenPref Results: talkPref Conclusion • Communication makes society more viable • High talking and listening preferences give agents a selective advantage • Higher cooperative pressure induces communication • Future work: other topologies, communication protocols