It is well-known that eliminating repeated states is essential for efficient search of state-space AND-OR graphs. The same technique has been found useful for searching beliefstate AND-OR graphs, which arise in nondeterministic partially observable planning problems and in partially observable games. Whereas physical states are viewed by search algorithms as atomic and admit only equality tests...

In this paper, we discuss how to test partially specified IOTS through lossless queues. A liberal assumption is made of the IOTS model by allowing both blocked and unspecified input actions. For testing IOTS through unbounded queues, we demonstrate that test cases can directly be derived from the specification when the transition coverage criterion is used, and we provide two test derivation al...

This paper presents an analysis of an electro-optical hazard alerting system based on intruder line-of-sight rate. We use a recently-developed airspace encounter model to analyze intruder line-of-sight rate behavior prior to near miss. We look at a simple hazard alerting system that alerts whenever the line-of-sight rate drops below some set threshold. Simulations demonstrate that such an appro...

In this article, we present an idea for solving deterministic partially observable markov decision processes (POMDPs) based on a history space containing sequences of past observations and actions. A novel and sound technique for learning a Q-function on history spaces is developed and discussed. We analyze certain conditions under which a history based approach is able to learn policies compar...

In this article we propose a qualitative ( ordi­ nal) counterpart for the Partially Observable Markov Decision Processes rnodel (POMDP) in which the uncertainty, as well as the prefer­ ences of the agent, are modeled by possibility distributions. This qualitative counterpart of the POMDP model relies on a possibilistic theory of decision under uncertainty, recently developed. One advantage of s...

A new policy iteration algorithm for partially observable Markov decision processes is presented that is simpler and more eecient than an earlier policy iteration algorithm of Sondik (1971,1978). The key simpliication is representation of a policy as a nite-state controller. This representation makes policy evaluation straightforward. The pa-per's contribution is to show that the dynamic-progra...