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**Published in:** LIPIcs, Volume 7, Technical Communications of the 26th International Conference on Logic Programming (2010)

Reasoning about social networks (labeled, directed, weighted graphs) is becoming increasingly important and there are now models of how certain phenomena (e.g. adoption of products/services by consumers, spread of a given disease) "diffuse" through the network. Some of these diffusion models can be expressed via generalized annotated programs (GAPs). In this paper, we consider the following problem: suppose we have a given goal to achieve (e.g. maximize the expected number of adoptees of a product or minimize the spread of a disease) and suppose we have limited resources to use in trying to achieve the goal (e.g. give out a few free plans, provide medication to key people in the SN) - how should these resources be used so that we optimize a given objective function related to the goal? We define a class of social network optimization problems (SNOPs) that supports this type of reasoning. We formalize and study the complexity of SNOPs and show how they can be used in conjunction with existing economic and disease diffusion models.

Paulo Shakarian, V.S. Subrahmanian, and Maria Luisa Sapino. Using Generalized Annotated Programs to Solve Social Network Optimization Problems. In Technical Communications of the 26th International Conference on Logic Programming. Leibniz International Proceedings in Informatics (LIPIcs), Volume 7, pp. 182-191, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2010)

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@InProceedings{shakarian_et_al:LIPIcs.ICLP.2010.182, author = {Shakarian, Paulo and Subrahmanian, V.S. and Sapino, Maria Luisa}, title = {{Using Generalized Annotated Programs to Solve Social Network Optimization Problems}}, booktitle = {Technical Communications of the 26th International Conference on Logic Programming}, pages = {182--191}, series = {Leibniz International Proceedings in Informatics (LIPIcs)}, ISBN = {978-3-939897-17-0}, ISSN = {1868-8969}, year = {2010}, volume = {7}, editor = {Hermenegildo, Manuel and Schaub, Torsten}, publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik}, address = {Dagstuhl, Germany}, URL = {https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ICLP.2010.182}, URN = {urn:nbn:de:0030-drops-25964}, doi = {10.4230/LIPIcs.ICLP.2010.182}, annote = {Keywords: Annotated logic programming, optimization queries, social networks} }

Document

**Published in:** LIPIcs, Volume 7, Technical Communications of the 26th International Conference on Logic Programming (2010)

Action-probabilistic logic programs (ap-programs) are a class of probabilistic logic programs that have been extensively used during the last few years for modeling behaviors of entities. Rules in ap-programs have the form "If the environment in which entity E operates satisfies certain conditions, then the probability that E will take some action A is between L and U". Given an ap-program, we are interested in trying to change the environment, subject to some constraints, so that the probability that entity E takes some action (or combination of actions) is maximized. This is called the Basic Probabilistic Logic Abduction Problem (Basic PLAP). We first formally define and study the complexity of Basic PLAP and then provide an exact (exponential) algorithm to solve it, followed by more efficient algorithms for specific subclasses of the problem. We also develop appropriate heuristics to solve Basic PLAP efficiently.

Gerardo Simari and V.S. Subrahmanian. Abductive Inference in Probabilistic Logic Programs. In Technical Communications of the 26th International Conference on Logic Programming. Leibniz International Proceedings in Informatics (LIPIcs), Volume 7, pp. 192-201, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2010)

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@InProceedings{simari_et_al:LIPIcs.ICLP.2010.192, author = {Simari, Gerardo and Subrahmanian, V.S.}, title = {{Abductive Inference in Probabilistic Logic Programs}}, booktitle = {Technical Communications of the 26th International Conference on Logic Programming}, pages = {192--201}, series = {Leibniz International Proceedings in Informatics (LIPIcs)}, ISBN = {978-3-939897-17-0}, ISSN = {1868-8969}, year = {2010}, volume = {7}, editor = {Hermenegildo, Manuel and Schaub, Torsten}, publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik}, address = {Dagstuhl, Germany}, URL = {https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ICLP.2010.192}, URN = {urn:nbn:de:0030-drops-25971}, doi = {10.4230/LIPIcs.ICLP.2010.192}, annote = {Keywords: Probabilistic Logic Programming, Imprecise Probabilities, Abductive Inference} }

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**Published in:** LIPIcs, Volume 11, Technical Communications of the 27th International Conference on Logic Programming (ICLP'11) (2011)

Annotated Probabilistic Temporal (APT) logic programs are a form of logic programs that allow users to state (or systems to automatically learn)rules of the form ``formula G becomes true K time units after formula F became true with L to U% probability.''
In this paper, we develop a theory of abduction for APT logic programs. Specifically, given an APT logic program Pi, a set of formulas H that can be ``added'' to Pi, and a goal G, is there a subset S of H such that Pi \cup S is consistent and entails the goal G? In this paper, we study the complexity of the Basic APT Abduction Problem (BAAP). We then leverage a geometric characterization of BAAP to suggest a set of pruning strategies when solving BAAP and use these intuitions to develop a sound and complete algorithm.

Cristian Molinaro, Amy Sliva, and V. S. Subrahmanian. Abduction in Annotated Probabilistic Temporal Logic. In Technical Communications of the 27th International Conference on Logic Programming (ICLP'11). Leibniz International Proceedings in Informatics (LIPIcs), Volume 11, pp. 240-250, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2011)

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@InProceedings{molinaro_et_al:LIPIcs.ICLP.2011.240, author = {Molinaro, Cristian and Sliva, Amy and Subrahmanian, V. S.}, title = {{Abduction in Annotated Probabilistic Temporal Logic}}, booktitle = {Technical Communications of the 27th International Conference on Logic Programming (ICLP'11)}, pages = {240--250}, series = {Leibniz International Proceedings in Informatics (LIPIcs)}, ISBN = {978-3-939897-31-6}, ISSN = {1868-8969}, year = {2011}, volume = {11}, editor = {Gallagher, John P. and Gelfond, Michael}, publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik}, address = {Dagstuhl, Germany}, URL = {https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ICLP.2011.240}, URN = {urn:nbn:de:0030-drops-31697}, doi = {10.4230/LIPIcs.ICLP.2011.240}, annote = {Keywords: Probabilistic Reasoning, Imprecise Probabilities, Temporal Reasoning, Abductive Reasoning} }