Abstract
Heterogeneous agents that execute in dynamic, uncertain, partially cooperative, partially adversely environments have to take their decisions rapidly with an incomplete knowledge of actual environment conditions. This paper discusses different level of abstractions in agent’s development for this type of domain, explains the principles of offline debugging and employs these principles in robotic agent’s teams through the use of a new visual debugging tool. We argue that during the development of such complex agents, understanding agent reasoning is of crucial importance to the developer and that such understanding can only be done, in most of the cases, using an offline analysis of the agent’s decisions. In order for the developer to rapidly perceive agent’s reasoning we advocate visual debugging of the agent knowledge and reasoning at several levels of abstraction and at several different functional views. These principles have been applied with success in the context of a robotic soccer 2D simulation league team through the development of tools and extensive use of these analysis principles.
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Lau, N., Reis, L.P., Certo, J. (2007). Understanding Dynamic Agent’s Reasoning. In: Neves, J., Santos, M.F., Machado, J.M. (eds) Progress in Artificial Intelligence. EPIA 2007. Lecture Notes in Computer Science(), vol 4874. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-77002-2_46
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DOI: https://doi.org/10.1007/978-3-540-77002-2_46
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