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Agent interface. The game system implements a communication interface through which bids, queries, and any other game interactions are transmitted. Our current game system is a generalization of the infrastructure we developed to operate an open research event called the “Trading Agent Competition” (TAC) [7]. Held annually since 2000, the TAC market game is based on a challenging scenario in the domain of travel shopping [8]. The TAC series has produced several interesting ideas about the design of trading agents [9, 10], and has served as a useful testbed for studying market-based resource allocation.

Since market prices are determined by the interaction of strategies, the actions chosen are ultimately a function of preferences and other agents’ strategies. However, rather than consider all possible strategies in the natural extensive-form description of the game, we fix the space of strategies to a finite set, and define payoffs directly as a function of strategy choices and preference assignments. Given that preferences are assigned probabilistically, this induces an expected payoff as a function of strategy profiles.

Advanced Menu of Adaptive Schemes (CI Tools) A human agent may combine several different CI tools into a hybrid system to learn and adapt. For example, a genetic neural fuzzy system is shown here. is no reason why they should all choose the same adaptive scheme, as each can make a choice independently from the menu shown in Figure 2. One recent research trend in computational intelligence involves the extensive use of hybrid systems, in which many tools work synergetically together as a multi- agent system.

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