Artificial Life: Borrowing from Biology: 4th Australian by Taras Kowaliw, Alan Dorin, Jon McCormack (auth.), Kevin

By Taras Kowaliw, Alan Dorin, Jon McCormack (auth.), Kevin Korb, Marcus Randall, Tim Hendtlass (eds.)

This e-book constitutes the refereed lawsuits of the 4th Australian convention on man made existence, ACAL 2009, held in Melbourne, Australia, in December 2009.

The 27 revised complete papers offered have been rigorously reviewed and chosen from 60 submissions. learn in Alife covers the most parts of organic behaviour as a metaphor for computational versions, computational types that reproduce/duplicate a organic behaviour, and computational types to unravel organic difficulties. hence, Alife good points analyses and realizing of existence and nature and is helping modeling organic platforms or fixing organic difficulties. The papers are equipped in topical sections on alife paintings, online game idea, evolution, advanced platforms, organic structures, social modelling, swarm intelligence, and heuristics.

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Additional info for Artificial Life: Borrowing from Biology: 4th Australian Conference, ACAL 2009, Melbourne, Australia, December 1-4, 2009. Proceedings

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The first one involves only players with pure strategies. That is, each agent always plays the cooperate or the defect action. In the second scenario, we also consider a third type of agent that follows a mixed strategy. A mixed strategy is an assignment of a probability to each pure strategy (see details below). The action taken by a mixed strategy agent i depends on the weights of the links wij it has established with each of its opponents j ∈ g for the current game g. The average link weight for player i in game g is then defined as: w i (g) = 1 |g| wij j∈g A mixed strategy i plays cooperatively in game g with probability: Pi (g) = wi (g)α + β w i (g)α + β + 1 With probability 1 − Pi (g), it plays defectively.

In contrast, an agent equipped with a mixed strategy plays a particular action based on a function of the reliability of other agents in its group in a given game. Thus, individual agents are able to adjust both these links and the action they play based on interactions with other agents. Detailed simulation experiments show that our model is able to promote higher levels of cooperation as compared with panmictic populations. An analysis of the social interactions between cooperative agents reveals high average clustering and associated single-to-broad-scale heterogeneity for relatively small values of N .

It is necessary to note that in the random neighbourhood structures, the neighbouring agents are being changed in every generation. (a) N = 4 (b) N = 5 Fig. 1. Fixed neighbourhood structures (a) N = 4 (b) N = 5 Fig. 2. 2 Strategy Representation There are various ways to represent the agent’s game-playing strategies. We have decided to adopt the representation developed by Yao and Darwen [6] as it is exponentially much shorter and easier to implement than the others. Under this representation, a history of l rounds for an agent can be represented as the combination of the following: ƒ l bits to represent the agent’s l previous actions.

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