TY - GEN
T1 - Computational complexity of planning based on partial information about the system's present and past states
AU - Baral, Chitta
AU - Tuan, Le Chi
AU - Trejo, Raul
AU - Kreinovich, Vladik
N1 - Publisher Copyright:
© Springer-Verlag Berlin Heidelberg 2000.
PY - 2000
Y1 - 2000
N2 - Planning is a very important AI problem, and it is also a very time-consuming AI problem. To get an idea of how complex dif- ferent planning problems are, it is useful to describe the computational complexity of different general planning problems. This complexity has been described for problems in which planning is based on the (complete or partial) information about the current state of the system. In real-life planning problems, we can often complement the incompleteness of our explicit knowledge about the current state by using the implicit knowl- edge about this state which is contained in the description of the system's past behavior. For example, the information about the system's past fail- ures is very important in planning diagnostic and repair. To describe planning which can use the information about the past, a special lan- guage L was developed in 1997 by C. Baral, M. Gelfond and A. Provetti. In this paper, we expand the known results about computational com- plexity of planning (including our own previous results) to this more general class of planning problems.
AB - Planning is a very important AI problem, and it is also a very time-consuming AI problem. To get an idea of how complex dif- ferent planning problems are, it is useful to describe the computational complexity of different general planning problems. This complexity has been described for problems in which planning is based on the (complete or partial) information about the current state of the system. In real-life planning problems, we can often complement the incompleteness of our explicit knowledge about the current state by using the implicit knowl- edge about this state which is contained in the description of the system's past behavior. For example, the information about the system's past fail- ures is very important in planning diagnostic and repair. To describe planning which can use the information about the past, a special lan- guage L was developed in 1997 by C. Baral, M. Gelfond and A. Provetti. In this paper, we expand the known results about computational com- plexity of planning (including our own previous results) to this more general class of planning problems.
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U2 - 10.1007/3-540-44957-4_59
DO - 10.1007/3-540-44957-4_59
M3 - Conference contribution
AN - SCOPUS:84867767010
T3 - Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)
SP - 882
EP - 896
BT - Computational Logic - CL 2000 - 1st International Conference, Proceedings
A2 - Lloyd, John
A2 - Dahl, Veronica
A2 - Furbach, Ulrich
A2 - Kerber, Manfred
A2 - Lau, Kung-Kiu
A2 - Palamidessi, Catuscia
A2 - Pereira, Luís Moniz
A2 - Sagiv, Yehoshua
A2 - Stuckey, Peter J.
PB - Springer Verlag
T2 - 1st International Conference on Computational Logic, CL 2000
Y2 - 24 July 2000 through 28 July 2000
ER -