S. Koenig and R. Simmons, `Solving robot navigation problems with initial pose uncertainty using real-time heuristic search', in Proc of AIPS-98, (1998).  Home/Search   Document Details and Download   Summary   Related Articles   Check

....reduced by providing the branching structure of the plan as an input to the planner. The problem of planning under partial observability has been deeply investigated in the framework of Partially Observable MDP (see, e.g. CKL94; HZ98; PB00] Methods that interleave planning and execution [KS98; GN93] can be considered alternative (and orthogonal) approaches to the problem of planning off line with large state spaces. However, these methods cannot guarantee to find a solution, unless assumptions are made about the domain. For instance, KS98] assumes safely explorable domains without ....

....that interleave planning and execution [KS98; GN93] can be considered alternative (and orthogonal) approaches to the problem of planning off line with large state spaces. However, these methods cannot guarantee to find a solution, unless assumptions are made about the domain. For instance, KS98] assumes safely explorable domains without cycles. GN93] describes an off line planning algorithm based on a breadth first search on an and or graph. The paper shows that the version of the algorithm that interleaves planning and execution is more efficient than the off line version, both ....

S. Koenig and R. Simmons. Solving robot navigation problems with initial pose uncertainty using real-time heuristic search. In Proceedings of the International Conference on Artificial Intelligence Planning and Scheduling, 1998.

....run time, e.g. just some variables can be observed in di erent situations. Under this hypothesis, known as partial observability , planning in deterministic domain is addressed by di erent techniques, see, e.g. Pryor Collins, 1996; Weld et al. 1998; Bonet Ge ner, 2000; Rintanen, 1999a; Koenig Simmons, 1998; Tovey Koenig, 2000 ] A limit case of planning under partial observability is conformant planning, where the assumption is that no information is available at run time, see, e.g. Smith Weld, 1998; Bonet Ge ner, 2000 ] The idea of Planning via (Symbolic) Model Checking has been rst ....

S. Koenig and R. Simmons. Solving robot navigation problems with initial pose uncertainty using real-time heuristic search. In Proceedings 2nd International Conference on AI Planning Systems (AIPS-98), 1998.

....The problem of planning under partial observability has been deeply investigated in the framework of Partially Observable MDP (see, e.g. Cassandra et al. 1994; Hansen and Zilberstein, 1998; Poupart and Boutilier, 2000] GPT follows this approach. Methods that interleave planning and execution [Koenig and Simmons, 1998; Genesereth and Nourbakhsh, 1993] can be considered alternative (and orthogonal) approaches to the problem of planning off line with large state spaces. However, these methods cannot guarantee to find a solution, unless assumptions are made about the domain. For instance, Koenig and Simmons, ....

....[Koenig and Simmons, 1998; Genesereth and Nourbakhsh, 1993] can be considered alternative (and orthogonal) approaches to the problem of planning off line with large state spaces. However, these methods cannot guarantee to find a solution, unless assumptions are made about the domain. For instance, [Koenig and Simmons, 1998] assumes safely explorable domains without cycles. Genesereth and Nourbakhsh, 1993] describes an off line planning algorithm based on a breadth first search on an and or graph. The paper shows that the version of the algorithm that interleaves planning and execution is more efficient than the ....

S. Koenig and R. Simmons. Solving robot navigation problems with initial pose uncertainty using real-time heuristic search. Proc. of AIPS-1998.

....as A [17] on a variety of search problems. Real time search methods have, for example, successfully been applied to traditional search prob lems [16] moving target search problems [8] STRIPS type planning problems [5] robot navigation and localization problems with initial pose uncertainty [13], totally observable Markov decision process problems [3] and partially observable Markov decision process problems [4] among others. A good overview of real time search methods is given in [7] and newer developments can be found in [10] We study two similar real time search methods that ants ....

S. Koenig and R.G. Simmons. Solving robot navigation problems with initial pose uncer- tainty using real-time heuristic search. In Proceedings of the International Conference on Artificial Intelligence Planning Systems, pages 154-153, 1998.

.... been applied to a variety of planning problems, including traditional search problems (Korf 1990) moving target search problems (Ishida Korf 1991) STRIPS type planning problems (Bonet, Loerincs, Geffner 1997) robot navigation and localization problems with initial pose uncertainty (Koenig Simmons 1998), robot exploration problems (Koenig 1999) totally observable Markov decision process problems (Barto, Bradtke, Singh 1995) and partially observable Markov decision process problems (Geffner Bonet 1998) Learning Real Time A (LRTA ) is probably the most popular real time search method (Korf ....

Koenig, S., and Simmons, R. 1998. Solving robot navigation problems with initial pose uncertainty using real-time heuristic search. In Proceedings of the International Conference on Artificial Intelligence Planning Systems, 154--153.

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Koenig, S., and Simmons, R. 1998a. Solving robot navigation problems with initial pose uncertainty using real-time heuristic search. In Proceedings of the International Conference on Artificial Intelligence Planning Systems, 154--153.

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Koenig, S. and Simmons, R.G. 1998a. Solving robot navigation problems with initial pose uncertainty using real-time heuristic search. In Proceedings of the International Conference on Artificial Intelligence Planning Systems. 154--153.

....(Korf 1990) movingtarget search problems (Ishida and Korf 1991) STRIPS type planning problems (Bonet et al. 1997) robot navigation and Copyright c fl1999, American Association for Artificial Intelligence (www.aaai.org) All rights reserved. localization problems with initial pose uncertainty (Koenig and Simmons 1998), totally observable Markov decision process problems (Barto et al. 1995) and partially observable Markov decision process problems (Geffner and Bonet 1998) among others. Despite this success of real time search methods, not much is known about their properties. They differ in this respect from ....

.... at the end of the pass 1 1 1 0 1 1 1 0 1 1 1 1 1 1 0 1 1 1 3 1 1 1 1 1 1 0 1 1 1 3 5 5 1 1 1 1 5 1 1 1 4 5 5 1 1 1 1 5 7 7 7 12 16 16 1 1 1 1 5 7 7 7 24 16 16 1 1 0 1 0 1 0 1 0 1 0 1 1 Figure 2: Node Counting has Exponential Runtime in Undirected Domains (m = 2; n = 18) and tedious (Szymanski and Koenig 1998). To convince the reader of the correctness of our proofs, however, we provide experimental results from a simulation study that confirm our analytical results. As part of the proof sketch, we study a tree with m 1 levels. Let u p;m (s) denote the total number of times that subroot s has been ....

Koenig, S. and Simmons, R.G. 1998. Solving robot navigation problems with initial pose uncertainty using real-time heuristic search. In Proceedings of the International Conference on Artificial Intelligence Planning Systems. 154--153.

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S. Koenig and R. Simmons, `Solving robot navigation problems with initial pose uncertainty using real-time heuristic search', in Proc of AIPS-98, (1998).

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