By Timothy C. Havens, James C. Bezdek, Marimuthu Palaniswami (auth.), Kurosh Madani, António Dourado Correia, Agostinho Rosa, Joaquim Filipe (eds.)
The current e-book contains a set of chosen prolonged papers from the second one overseas Joint convention on Computational Intelligence (IJCCI 2010), held in Valencia, Spain, from 24 to 26 October 2010. The convention was once composed by way of 3 co-located meetings: The foreign convention on Fuzzy Computation (ICFC), the foreign convention on Evolutionary Computation (ICEC), and the overseas convention on Neural Computation (ICNC). fresh progresses in clinical advancements and functions in those 3 components are mentioned during this ebook. IJCCI obtained 236 submissions, from forty nine international locations, in all continents. After a double blind paper evaluate played via this system Committee, merely 30 submissions have been authorised as complete papers and hence chosen for oral presentation, resulting in a whole paper attractiveness ratio of 13%. extra papers have been approved as brief papers and posters. an extra choice used to be made after the convention, dependent additionally at the evaluation of presentation caliber and viewers curiosity, in order that this publication contains the prolonged and revised models of the superior papers of IJCCI 2010. dedication to top quality criteria is a big predicament of IJCCI that would be maintained within the subsequent variants, contemplating not just the stringent paper popularity ratios but in addition the standard of this system committee, keynote lectures, participation point and logistics.
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Additional info for Computational Intelligence: Revised and Selected Papers of the International Joint Conference, IJCCI 2010, Valencia, Spain, October 2010
It is possible that one population may have a superior strategy, and that the agents from this population out-rank all agents from all other populations. Regardless, agents are separated by population for Countering Evolutionary Forgetting in No-Limit Texas Hold’em Poker Agents 43 evaluation and evolution, in order to preserve any unique exploration paths that alternate populations might be exploring. Like halls of fame, the main strategy of co-evolution is a deepening of the competition. By having seperately evolving populations, the agents are exposed to a more varied set of strategies, and thus can produce more robust strategies.
Evolution is found to improve the skill level of the agents, allowing them to play better than agents developed through other means. 4 Methodology Our agents use a 35-20-3 feedforward neural network to learn how to play No-Limit Texas Hold’em. This type of network has three levels, the input level, the hidden level, and the output level. Thirty-five values, which will be explained in Sect. 1, are taken from the current game state. These values are combined and manipulated using weighted connections to twenty nodes on the hidden level of the network.
The agents also keep track of their own aggressiveness, with the goal of preventing predictability. If an agent becomes too predictable, they can be taken advantage of. Agents can then make decisions counter to their decisions to throw off opponents. Aggressiveness over the Short-Term. Although agents will generally fall into an overall pattern, it is possible to ignore that pattern for short periods of time. Thus, agents keep track of short-term aggressiveness of their opponents. Short-term aggressiveness is calculated in the same way as long-term aggressiveness, but only over the last ten hands of a particular tournament.
Computational Intelligence: Revised and Selected Papers of the International Joint Conference, IJCCI 2010, Valencia, Spain, October 2010 by Timothy C. Havens, James C. Bezdek, Marimuthu Palaniswami (auth.), Kurosh Madani, António Dourado Correia, Agostinho Rosa, Joaquim Filipe (eds.)