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1991, A bottom-up mechanism for behaviour selection in an artificial creature . In J. A. W. Wilson, editors, Proceedings of the First International Conference on Simulation of Adaptive Behaviour: From Animals to Animats , 238-246. MIT Press/Bradford Books. Moffat, D. , 1995. Where there's a will there's an agent. In M. Wooldridge and N. R Jennings, editors, Intelligent Agents: Theories, Architectures, and Languages, Lecture Notes in Artificial Intelligence 890, 245-260. Springer. H. , 1993. Analysis of a model of emotions.

Let p define a function that maps the spaces of X and F to a positive real number, p : X x F ~ R". The function, p(x,f), indicates the performance per unit time of all agents in achieving their goal under configuration (x,f), that is, in situation x under DMF f The function, p(x,f), is a "penalty" function in the sense that higher values will correspond to worse performance. Therefore p(x, I) should be minimized to maximize performance. Let P define a function that maps the spaces of SK and A K to a positive real number, P: SK x A K ~ jR+.

In Prospects for Artificial Intelligence: Proceedings ofAlSB93, 219-228, Birmingham. J. , 1995. Goal creation in motivated agents. J. R, editors, Intelligent Agents: Theories, Architectures, and Languages, Lecture Notes in Artificial Intelligence 890, 277-290. Springer. J. , 1996. Alarms: An implementation of motivated agency. In M. P. Muller, and M. Tambe, editors, Intelligent Agents: Theories, Architectures, and Languages, Lecture Notes in Artificial Intelligence 1037, 219-234. Springer. , 1991.

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