Tractability and artificial intelligence

Bylander, Tom

doi:10.1080/09528139108915289

Cited by 4 publications

(4 citation statements)

References 16 publications

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“…For example, in principle many problems (such as playing chess) can be solved by exhaustive search, but we cannot afford the time it requires -exponential algorithms easily produce "combinatorial explosion," so they make no practical sense. That is why some authors treat tractability as a central issue in AI [Bylander, 1991, Levesque, 1989.…”

Section: Algorithm and Intelligencementioning

confidence: 99%

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Rigid Flexibility

2006

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Section: Algorithm and Intelligencementioning

confidence: 99%

“…However, the majority of the AI researchers continue to apply the theory of computation to AI, and analyze problems in terms of computability and computational complexity, as exemplified by [Bylander, 1991, Edmonds, 2000, Hutter, 2001, Levesque, 1989, Littman et al, 1998, Valiant, 1984.…”

Section: Algorithm and Intelligencementioning

confidence: 99%

Rigid Flexibility

2006

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“…However, there is a large literature on the philosophical implications of the Turing Test, see for example [21,9,7]. While in a very different context complexity theory has become prominent :in the study of artificial intelligence, see for example [5].…”

Section: Previous Workmentioning

confidence: 99%

A formalization of the Turing test

Bradford¹,

Wollowski²

1995

SIGART Bull.

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Alan Turing proposed an interactive test to replace the question "Can machines think?" This test has become known as the Turing Test and its validity for determining intelligence or thinking is still in question.Struggling with the validity of long proofs, program correctness, computational complexity and cryptography, theoreticians developed interactive proof systems. By formalizing the Turing Test as an interactive proof system and by employing results from complexity theory, this paper investigates the power and limitations of the Turing Test. In particular, assuming the notion of completeness for standard complexity classes carries over faithfully to human cognition, then we can say: if human intelligence subsumes machine intelligence, and human intelligence is not simulatable by any bounded machine, then the Turing Test can distinguish humans and machines to within arbitrarily high probability. This paper makes no claim about the Turing Test's sufficiency to distinguish humans and machines. Rather, through its formalization this paper gives several ramifications involving the acceptance or rejection of the Turing Test as sufficient for making any such distinction.

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“…This is the complexity of planning in STRIPS [8], which can be seen as a special case of the setting considered here -reachability of a desired state of affairs is the only kind of goal considered in STRIPS; moreover, only certain transition systems are (compactly) representable.…”

Section: Theorem 2 Planning In the Setting Above Is Decidable In Nlomentioning

confidence: 99%

Automata-Theoretic Approach to Planning for Temporally Extended Goals

Giacomo

Vardi

2000

Recent Advances in AI Planning

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Abstract.We study an automata-theoretic approach to planning for temporally extended goals. Specifically, we devise techniques based on nonemptiness of Büchi automata on infinite words, to synthesize sequential and conditional plans in a generalized setting in which we have that: goals are general temporal properties of desired execution; dynamic systems are represented by finite transition systems; incomplete information on the initial situation is allowed; and states are only partially observable. We prove that the techniques proposed are optimal wrt the worst case complexity of the problem. Thanks to the scalability of the nonemptiness algorithms, the techniques presented here promise to be applicable to fairly large systems, notwithstanding the intrinsic complexity of the problem.

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Tractability and artificial intelligence

Cited by 4 publications

References 16 publications

Rigid Flexibility

Rigid Flexibility

A formalization of the Turing test

Automata-Theoretic Approach to Planning for Temporally Extended Goals

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