MANCaLog: A Logic for Multi-Attribute Network Cascades (Technical Report)

The modeling of cascade processes in multi-agent systems in the form of complex networks has in recent years become an important topic of study due to its many applications: the adoption of commercial products, spread of disease, the diffusion of an idea, etc. In this paper, we begin by identifying a desiderata of seven properties that a framework for modeling such processes should satisfy: the ability to represent attributes of both nodes and edges, an explicit representation of time, the ability to represent non-Markovian temporal relationships, representation of uncertain information, the ability to represent competing cascades, allowance of non-monotonic diffusion, and computational tractability. We then present the MANCaLog language, a formalism based on logic programming that satisfies all these desiderata, and focus on algorithms for finding minimal models (from which the outcome of cascades can be obtained) as well as how this formalism can be applied in real world scenarios. We are not aware of any other formalism in the literature that meets all of the above requirements

Heuristic Ranking in Tightly Coupled Probabilistic Description Logics

The Semantic Web effort has steadily been gaining traction in the recent years. In particular,Web search companies are recently realizing that their products need to evolve towards having richer semantic search capabilities. Description logics (DLs) have been adopted as the formal underpinnings for Semantic Web languages used in describing ontologies. Reasoning under uncertainty has recently taken a leading role in this arena, given the nature of data found on theWeb. In this paper, we present a probabilistic extension of the DL EL++ (which underlies the OWL2 EL profile) using Markov logic networks (MLNs) as probabilistic semantics. This extension is tightly coupled, meaning that probabilistic annotations in formulas can refer to objects in the ontology. We show that, even though the tightly coupled nature of our language means that many basic operations are data-intractable, we can leverage a sublanguage of MLNs that allows to rank the atomic consequences of an ontology relative to their probability values (called ranking queries) even when these values are not fully computed. We present an anytime algorithm to answer ranking queries, and provide an upper bound on the error that it incurs, as well as a criterion to decide when results are guaranteed to be correct.Comment: Appears in Proceedings of the Twenty-Eighth Conference on Uncertainty in Artificial Intelligence (UAI2012

Rational decision making in autonomous agents

Making rational decisions is one of the key elements in the design of autonomous agents with successful behavior. Even though there have been many proposals for the support of decision making, most of them can be described either as descriptive or prescriptive. The main goal of our work is to establish the relationship between two of these models, namely bdi and mdps, in order to gain further understanding of how decisions in one model are viewed from the point of view of the other. This goal is important for the development of agent design strategies that unite the best of both worlds.Eje: Inteligencia artificial distribuida, aspectos teóricos de la inteligencia artificial y teoría de computaciónRed de Universidades con Carreras en Informática (RedUNCI

Non prioritized belief revision with ansprolog

One of the key aspects in the design of an architecture for autonomous agents is the way in which beleifs are revised in the face of new information. An intelligent agent must be prepared to handle new information (be it sensory input, communication with other agents, etc). The areas of Knowledge Representation and Reasoning and Belief Revision deal with the issues of how beliefs are represented, how they are used in reasoning processes, and how a current body of beliefs reacts to the appearance of new information. There are many problems that must be solved in order to e®ectively manage a body of knowledge which, more often than not, may be incomplete or even inconsistent. The adequate solution to these problems is an essential feature of autonomy, because an agent must always be up to date in order to behave accordingly.Eje: Inteligencia artificial distribuida, aspectos teóricos de la inteligencia artificial y teoría de computaciónRed de Universidades con Carreras en Informática (RedUNCI

Non prioritized answer set revision

In this paper, we build on previous work on Belief Revision operators based on the use of logic programming with Answer Set semantics as a representation language. We present a set of postulates for Answer Set Revision with respect to a set of sentences and with respect to explanations. We focus on the non-prioritized revision operator with respect to explanations, or arguments, which is intended to model situations in which agents revise their knowledge as a result of dialogues with other agents in a multi-agent setting.Eje: V - Workshop de agentes y sistemas inteligentesRed de Universidades con Carreras en Informática (RedUNCI

On the problem of comparing two models for rational decision making in autonomous agents

The main objective of this work is to establish a basis for comparing BDI agents with the optimality of decision theory models such as POMDPs. It is argued that a direct comparison is not possible due to the intractability of the algorithms used for solving POMDPs, and therefore an approximation must be used. We propose the reduction of the state space, and the combination of sub-solutions as the main approaches towards this goal. Throughout this work, the tileworld testbed is used as a frame of reference for the discussion of the various concepts.Eje: Agentes y Sistemas Inteligentes (ASI)Red de Universidades con Carreras en Informática (RedUNCI

Belief dynamics and explanations in ansprolog

Knowledge representation models are very important in the design of intelligent agents because they provide with mechanisms to manage beliefs and their dynamics. In this paper, we propose the use of AnsProlog* as a knowledge representation language, and develop a Non Prioritized Belief Revision operator based on the Answer Set semantics and the use of explanations. This operator is suitable for multiagent environments, in which agents can exchange information by having dialogues which explain their respective beliefs. A simple, yet complete example follows the presentation of this operator.Eje: Agentes y Sistemas Inteligentes (ASI)Red de Universidades con Carreras en Informática (RedUNCI

Reasoning about Complex Networks: A Logic Programming Approach

Reasoning about complex networks has in recent years become an important topic of study due to its many applications: the adoption of commercial products, spread of disease, the diffusion of an idea, etc. In this paper, we present the MANCaLog language, a formalism based on logic programming that satisfies a set of desiderata proposed in previous work as recommendations for the development of approaches to reasoning in complex networks. To the best of our knowledge, this is the first formalism that satisfies all such criteria. We first focus on algorithms for finding minimal models (on which multi-attribute analysis can be done), and then on how this formalism can be applied in certain real world scenarios. Towards this end, we study the problem of deciding group membership in social networks: given a social network and a set of groups where group membership of only some of the individuals in the network is known, we wish to determine a degree of membership for the remaining group-individual pairs. We develop a prototype implementation that we use to obtain experimental results on two real world datasets, including a current social network of criminal gangs in a major U.S.\ city. We then show how the assignment of degree of membership to nodes in this case allows for a better understanding of the criminal gang problem when combined with other social network mining techniques -- including detection of sub-groups and identification of core group members -- which would not be possible without further identification of additional group members.Comment: arXiv admin note: substantial text overlap with arXiv:1301.030

MANCaLog: A Logic for Multi-Attribute Network Cascades (Extended Abstract)

A full version of this abstract can be found at http://arxiv.org/abs/1301.0302.Cascading processes on a network have been studied in a variety of disciplines, including computer science [3], biol- ogy [4], sociology [2], and economics [5]. Much existing work in this area is based on pre-existing models. However, recent examinations of social networks { both analysis of large data sets and experimental { have indicated that there may be additional factors to consider that are not taken into account by these models [1]..