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Like humans, autonomous agents will need to operate in physical settings that involve competing objectives which may be incompatible and vary in importance over time. They will also need to reason about both qualitative relations and quantitative attributes to produce behavior that is appropriate to the situation. In this paper, we report PUG, a(More)
In this paper, we discuss a computational approach to the cognitive task of social planning. First, we specify a class of planning problems that involve an agent who attempts to achieve its goals by altering other agents' mental states. Next, we describe SFPS, a flexible problem solver that generates social plans of this sort, including ones that include(More)
Markov decision processes (MDPs) are applied as a standard model in Artificial Intelligence planning. MDPs are used to construct optimal or near optimal policies or plans. One area that is often missing from discussions of planning under stochastic environment is how MDPs handle safety constraints expressed as probability of reaching threat states. We(More)
Progress has been made recently in developing techniques to automatically generate effective heuristics. These techniques typically aim to reduce the size of the search tree, usually by combining more primitive heuristics. However, simply reducing search tree size is not enough to guarantee that problems will be solved more quickly. We describe a new(More)