Knowledge preconditions for actions and plans
Distributed Artificial Intelligence
Planning and control
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According to the utilitarian paradigm, an autonomous intelligent agent's interactions with the environment should be guided by the principle of expected utility maximization. We apply this paradigm to reasoning about an agent's physical actions and exploratory behavior in urgent, time-constrained situations. We model an agent's knowledge with a temporalized version of Kripke structures--as a set of branching time lines described by fluents, with accessibility relations holding among the states comprising the time lines. We describe how to compute utility based on this model which reflects the urgency that the environment imposes on time. Since the physical and exploratory actions that an agent could undertake transform the model of branching time lines in specific ways, the expected utilities of these actions can be computed, dictating rational tradeoffs among them depending on the agent's state of knowledge and the urgency of the situation.