2008
DOI: 10.1007/978-3-540-87993-0_28
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On the Relationship between Hybrid Probabilistic Logic Programs and Stochastic Satisfiability

Abstract: In this paper we study the relationship between Stochastic Satisfiability (SSAT) (Papadimitriou 1985;Littman, Majercik, & Pitassi 2001) and Extended Hybrid Probabilistic Logic Programs (EHPP) with probabilistic answer set semantics (Saad 2006). We show that any instance of SSAT can be modularly translated into an EHPP program with probabilistic answer set semantics. In addition, we show that there is no modular mapping from EHPP to SSAT. This shows that EHPP is more expressive than SSAT from the knowledge repr… Show more

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Cited by 7 publications
(7 citation statements)
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“…Our approach is similar in spirit to [22] in the sense that both approaches are logic based approaches. However, it has been shown in [29] that NHPLP is more expressive than stochastic satisfiability from the knowledge representation point of view. In [15], based on first-order logic programs without nonmonotonic negation, a first-order logic representation of MDP has been described.…”
Section: Conclusion and Related Workmentioning
confidence: 99%
See 1 more Smart Citation
“…Our approach is similar in spirit to [22] in the sense that both approaches are logic based approaches. However, it has been shown in [29] that NHPLP is more expressive than stochastic satisfiability from the knowledge representation point of view. In [15], based on first-order logic programs without nonmonotonic negation, a first-order logic representation of MDP has been described.…”
Section: Conclusion and Related Workmentioning
confidence: 99%
“…The choice of normal hybrid probabilistic logic programs (NHPLP) to solve reinforcement learning problems in MDP environment is based on that; NHPLP is nonmonotonic, therefore more suitable for knowledge representation and reasoning under uncertainty; NHPLP subsumes classical normal logic programs with classical answer set semantics [7], a rich knowl-edge representation and reasoning framework, and inherits its knowledge representation and reasoning capabilities including the ability to represent and reason about domain-specific knowledge; NHPLP has been shown applicable to a variety of fundamental probabilistic reasoning problems including probabilistic planning [28], contingent probabilistic planning [31], the most probable explanation in belief networks, the most likely trajectory in probabilistic planning, and Bayesian reasoning [29].…”
Section: Introductionmentioning
confidence: 99%
“…, of the elevator domain described in Example 1 is given as follows, where consists of the following rules, along with the rules (18), (19), (20), (21)…”
Section: Example 2 the Normal Logic Program Encodingmentioning
confidence: 99%
“…A logical framework to model-based reinforcement learning has been proposed in [19] that overcomes the representational limitations of dynamic programming methods and capable of representing domain specific knowledge. The framework in [19] is based on the integration of model-based reinforcement learning in MDP environment with normal hybrid probabilistic logic programs with probabilistic answer set semantics [23] that allows representing and reasoning about a variety of fundamental probabilistic reasoning problems including probabilistic planning [18], contingent probabilistic planning [21], the most probable explanation in belief networks, and the most likely trajectory [20].…”
Section: Introductionmentioning
confidence: 99%
“…The importance of the probability answer set programming frameworks of [Saad and Pontelli, 2006;Saad, 2007a] lies in the fact that the probability answer set programming frameworks of [Saad and Pontelli, 2006;Saad, 2007a] have been shown applicable to a variety of fundamental probabilistic reasoning tasks. These probabilistic reasoning tasks include, but are not limited to, probabilistic planning [Saad, 2007b], probabilistic planning with imperfect sensing actions [Saad, 2009], reinforcement learning in MDP environments [Saad, 2008a], reinforcement learning in POMDP environments , and Bayes reasoning . Moreover, in [Saad, 2008b] it has been proved that stochastic satisfiability (SSAT) can be modularly encoded as probability answer set programs with probability answer set semantics, therefore, the applicability of SSAT to variety of fundamental probabilistic reasoning tasks also carry over to probability answer set programming [Saad and Pontelli, 2006;Saad, 2007a].…”
Section: Introductionmentioning
confidence: 99%