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Artificial Intelligence
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Toward a motivated BDI agent using attributes embedded in mental states
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Agents (in AI and DAI) are founded upon theories related to mental states and to the notion of architecture. However, there is still no consensus, or sufficient knowledge, to formulate a satisfactory theory which would define mental states, relating them to architectures and agent behaviour. The paper is located in this context and presents a theory in which a space of mental states is built up on types of mental states which are defined from a set of basic attributes which are: an External Content (a declaration about a situation in the world); criterions to determine the unsatisfaction, uncertainty, urgency, insistence, intensity and importance associated to a mental state; laws of causality through which a mental state can produce another; and mechanisms for provoking, selecting, suspending and interrupting the processing of a mental state. A space for possible agents' architectures is built up on that mental states space. So, from these two spaces, the paper presents a methodology to define and compare agents' architectures and to understand and produce the corresponding agents behaviour. In addition, it is shown that this methodology is suitable for agent programming based on the Object Oriented Programming paradigm.