Finding Most Probable Worlds of Probabilistic Logic Programs

Khuller, Samir; Martinez, Vanina; Nau, Dana; Simari, Gerardo I.; Sliva, Amy; Subrahmanian, V. S.

doi:10.1007/978-3-540-75410-7_4

Cited by 19 publications

(11 citation statements)

References 11 publications

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“…The naive approach to solving this problem as described in [11,12] is to derive a linear program from the ap-program and situation S, and to try to compute the probability of world w for each and every world w. This approach has two fundamental problems. First, the size of the linear program is exponential in the number of actions considered.…”

Section: Introductionmentioning

confidence: 99%

“…Action probabilistic logic program (ap-programs for short) [11,12] provide a logic programming paradigm through which we can develop stochastic models of the behavior of real world organizations without making any assumptions about independence of events and/or conditions. This is very important in many applications-for instance, our group has built what are now widely used models of the behaviors of various terror groups around the world [19][20][21].…”

Section: Introductionmentioning

confidence: 99%

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Focused most probable world computations in probabilistic logic programs

Simari

Martínez

Sliva

et al. 2012

Ann Math Artif Intell

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The "Most Probable World" (MPW) problem in probabilistic logic programming (PLPs) is that of finding a possible world with the highest probability. Past work has shown that this problem is computationally intractable and involves solving exponentially many linear programs, each of which is of exponential size. In this paper, we study what happens when the user focuses his interest on a set of atoms in such a PLP. We show that we can significantly reduce the number of worlds to be considered by defining a "reduced" linear program whose solution is in one-one correspondence with the exact solution to the MPW problem. However, the problem is still intractable. We develop a Monte Carlo sampling approach that enables us to build a quick approximation of the reduced linear program that allows us to estimate (inexactly) the solution to the MPW problem. We show experimentally that our approach works well in practice, scaling well to problems where the exact solution is intractable to compute.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Focused most probable world computations in probabilistic logic programs

Simari

Martínez

Sliva

et al. 2012

Ann Math Artif Intell

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“…Stochastic Opponent Modeling Agents introduced in [1,2,3] were introduced as a paradigm for reasoning about any group G in the world, irrespective of whether the group is a terror group, a social organization, a political party, a religious group, a militia, or an economic organization. SOMA-rules have been used to encode the behavior of players in the Afghan drug economy [4] as well as various tribes along the Pakistan-Afghanistan border [5].…”

Section: Introductionmentioning

confidence: 99%

Stochastic Opponent Modeling Agents: A Case Study with Hezbollah

Mannes¹,

Michael²,

Pate³

et al. 2008

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Stochastic Opponent Modeling Agents (SOMA) have been proposed as a paradigm for reasoning about cultural groups, terror groups, and other socioeconomic-political-military organizations worldwide. In this paper, we describe a case study that shows how SOMA was used to model the behavior of the terrorist organization, Hezbollah. Our team, consisting of a mix of computer scientists, policy experts, and political scientists, were able to understand new facts about Hezbollah of which even seasoned Hezbollah experts may not have been aware. This paper briefly overviews SOMA rules, explains how more than 14,000 SOMA rules for Hezbollah were automatically derived, and then describes a few key findings about Hezbollah, enabled by this framework.

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“…The Stochastic Opponent Modeling Agents (SOMA) framework, introduced in [2,3,4], has been proposed as a general paradigm for reasoning about the possible behaviors of any group G, irrespective of whether the group is a terror group, a social organization, a political party, a religious group, a militia, or an economic organization. The SOMA system provides a probabilistic logic representation of group behaviors and several methods for reasoning about the types of actions a group may take in a given situation.…”

Section: Soma Rule Derivation Methodologymentioning

confidence: 99%

“…Stochastic Opponent Modeling Agents introduced in [2,3,4] were introduced as a paradigm for reasoning about any group G in the world, irrespective of whether the group is a terror group, a social organization, a political party, a religious group, a militia, or an economic organization. SOMA-rules have been used to encode the behavior of players in the Afghan drug economy [5] as well as various tribes along the Pakistan-Afghanistan border [6].…”

Section: Introductionmentioning

confidence: 99%

Stochastic Opponent Modeling Agents: A Case Study with Hezbollah

Mannes

Michael

Pate

et al.

Social Computing, Behavioral Modeling, and Prediction

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Stochastic Opponent Modeling Agents (SOMA) have been proposed as a paradigm for reasoning about cultural groups, terror groups, and other socio-economic-political-military organizations worldwide. In this paper, we describe a case study that shows how SOMA was used to model the behavior of the terrorist organization, Hamas. Our team, consisting of a mix of computer scientists, policy experts, and political scientists, were able to understand new facts about Hamas of which even seasoned Hamas experts may not have been aware. This paper briefly overviews SOMA rules, explains how several thousand SOMA rules for Hamas were automatically derived, and then describes a few key findings about Hamas, enabled by this framework.

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Finding Most Probable Worlds of Probabilistic Logic Programs

Cited by 19 publications

References 11 publications

Focused most probable world computations in probabilistic logic programs

Focused most probable world computations in probabilistic logic programs

Stochastic Opponent Modeling Agents: A Case Study with Hezbollah

Stochastic Opponent Modeling Agents: A Case Study with Hezbollah

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