Gerardo I. Simari

Computing most probable worlds of action probabilistic logic programs: scalable estimation for 1030,000 worlds

The semantics of probabilistic logic programs (PLPs) is usually given through a possible worlds semantics. We propose a variant of PLPs called action probabilistic logic programs or -programs that use a two-sorted alphabet to describe the conditions under which certain real-world entities take certain actions. In such applications, worlds correspond to sets of actions these entities might take. Thus, there is a need to find the most probable world (MPW) for -programs. In contrast, past work on PLPs has primarily focused on the problem of entailment. This paper quickly presents the syntax and semantics of -programs and then shows a naive algorithm to solve the MPW problem using the linear program formulation commonly used for PLPs. As such linear programs have an exponential number of variables, we present two important new algorithms, called

On the relationship between MDPs and the BDI architecture

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Inconsistency handling in Datalog+/- ontologies

and

How Dirty Is Your Relational Database? An Axiomatic Approach

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CONVEX: Similarity-Based Algorithms for Forecasting Group Behavior

to solve the MPW problem exactly. Both these algorithms can significantly reduce the number of variables in the linear programs. Subsequently, we present a “binary” algorithm that applies a binary search style heuristic in conjunction with the Naive,

Aggregate Query Answering under Uncertain Schema Mappings

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Annotated probabilistic temporal logic

and

Complexity of Inconsistency-Tolerant Query Answering in Datalog+/–

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The SOMA Terror Organization Portal (STOP): social network and analytic tools for the real-time analysis of terror groups

algorithms to quickly find worlds that may not be “most probable.” We experimentally evaluate these algorithms both for accuracy (how much worse is the solution found by these heuristics in comparison to the exact solution) and for scalability (how long does it take to compute). We show that the results of

Annotated Probabilistic Temporal Logic: Approximate Fixpoint Implementation

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