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M. Shimbo, T. Ishida (2003). Controlling the Learning Process of Real-Time Heuristic Search. Artificial Intelligence, 146, 1–41. 66. V. Bulitko, G. Lee (2006). Learning in Real-Time Search: A Unifying Framework. J. Artificial Intelligence Research, 25, 119–157. 67. V. Bulitko, N. Sturtevant, J. Lu, T. Yau (2007). Graph Abstraction in Real-Time Heuristic Search. J. Artificial Intelligence Research, 30, 51–100. 68. C. Derman (1970). Finite State Markov Decision Processes. Academic Press: New York.
We refer to these updated probabilities as the observed probabilities and denote them by pt (xi ), xi ∈ X. If the target is static and this is known to the search agent, then the observed probabilities can be considered as equivalent to the location probabilities at the next time t + 1, pt+1 (xi ) = pt (xi ), xi ∈ X. In the case of search for a moving target, another layer of complexity is added to the search problem. Then the search agent has to apply some known (or estimated) rule of the target’s movement to the observed probabilities and obtain the next time step, namely, the estimated location probabilities pt+1 (xi ), xi ∈ X.
On the basis of such a relation, some authors [11] apply the term ‘search density’ immediately to the detection function ϕκ without an explicit definition of the search effort κ t . Such an approach is effective and clear if each observed area A consists of a single point only. An example that implements such an assumption is considered below. 2 Target location density for a Markovian search Let us consider a search for a Markovian target in a discrete sample space X = {x1 , x2 , . . , xn }, while the actions of the search agent and the target are conducted in discrete times t = 0, 1, 2, .