By Geoffrey Webb, Xinghuo Yu
This ebook constitutes the refereed complaints of the seventeenth Australian convention on synthetic Intelligence, AI 2004, held in Cairns, Australia, in December 2004.
The seventy eight revised complete papers and sixty two revised brief papers offered have been conscientiously reviewed and chosen from 340 submissions. The papers are equipped in topical sections on brokers; biomedical functions; machine imaginative and prescient, photograph processing, and development popularity; ontologies, wisdom discovery and information mining; normal language and speech processing; challenge fixing and reasoning; robotics; and tender computing.
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Extra resources for AI 2004: Advances in Artificial Intelligence: 17th Australian Joint Conference on Artificial Intelligence, Cairns, Australia, December 4-6, 2004, Proceedings
For the current problem, obviously individuals that can robustly report damage to the portal in minimum time and with low communication costs will score highly. We define several such functions as follows. 34 J. Li, Y. Guo, and G. Poulton Where is the fitness value, N is repeat time for each individual in the GA, M is the number of neighbours for each event. This fitness function only judges whether the damage report has arrived at the portal or not. Time and communications costs are ignored. A large penalty is given for any signals not reaching the portal.
Darwen, P. : On Evolving Robust Strategies for Iterated Prisoner’s Dilemma, Progress in Evolutionary Computation, Lecture Notes in Artificial Intelligence, Vol. 956. Springer-Verlag, Heidelberg Germany (1995) 276-292 7. : A Computational Evolutionary Approach to Evolving Game Strategy and Cooperation, IEEE Transactions on Systems, Man and Cybernetics, Part B, Vol. 32, No. 5 (2002) 498-502 8. : Coalition Formation among Autonomous Agents: Strategies and Complexity, Fifth European Workshop on Modeling Autonomous Agents in a MultiAgent World, Springer-Verlag, Heidelberg Germany (1993) 56-72 Agent-Based Evolutionary Labor Market Model with Strategic Coalition 13 9.
Fig. 2. Variation of average payoffs in a high job concentration. Employers get more payoffs than workers regardless of a coalition (Compare the average payoffs of two figures). 2 Balanced Job Concentration Table 4 depicts the experimental results of a balanced job concentration with each job capacity. As shown in the table, although a job concentration is balanced the behavioral patterns of agents can be varied because work offer quota (wq) and work acceptance quota (eq) are different. When a job capacity is tight and a coalition is not allowed, workers act inactively in order not to lose the refusal payoff against employers (worker 38%).
AI 2004: Advances in Artificial Intelligence: 17th Australian Joint Conference on Artificial Intelligence, Cairns, Australia, December 4-6, 2004, Proceedings by Geoffrey Webb, Xinghuo Yu