Marcela Capobianco

Argumentation and the Dynamics of Warranted Beliefs in Changing Environments

Abstract:One of the most difficult problems in Multi-Agent Systems (MAS) involves representing the knowledge and beliefs of an agent which performs its tasks in a dynamic environment. New perceptions modify this agent’s current knowledge about the world, and consequently its beliefs about it also change. Such a revision and update process should be performed efficiently by the agent, particularly in the context of real-time constraints. In the last decade argumentation has evolved as a successful approach to formalize defeasible, commonsense reasoning, gaining wide acceptance in the MAS community by providing tools for designing and implementing features, which characterize reasoning capabilities in rational agents. In this paper we present a new argument-based formalism specifically designed for representing knowledge and beliefs of agents in dynamic environments, called Observation-based Defeasible Logic Programming (ODeLP). A simple but effective perception mechanism allows an ODeLP-based agent to model new incoming perceptions, and modify the agent’s knowledge about the world accordingly. In addition, in order to improve the reactive capabilities of ODeLP-based agents, the process of computing beliefs in a changing environment is made computationally attractive by integrating a “dialectical database” with the agent’s program, providing pre-compiled information about previous inferences. We present algorithms for managing dialectical databases as well as examples of their use in the context of real-world problems.

An argument-based framework to model an agent’s beliefs in a dynamic environment

One of the most difficult problems in multiagent systems involves representing knowledge and beliefs of agents in dynamic environments. New perceptions modify an agent’s current knowledge about the world, and consequently its beliefs. Such revision and updating process should be performed efficiently by the agent, particularly in the context of real time constraints. This paper introduces an argument-based logic programming language called Observation-based Defeasible Logic Programming (ODeLP). An ODeLP program is used to represent an agent’s knowledge in the context of a multiagent system. The beliefs of the agent are modeled with warranted goals computed on the basis of the agent’s program. New perceptions from the environment result in changes in the agent’s knowledge handled by a simple but effective updating strategy. The process of computing beliefs in a changing environment is made computationally attractive by integrating a “dialectical database” with the agent’s program, providing precompiled information about inferences. We present algorithms for creation and use of dialectical databases.

Argument-based mixed recommenders and their application to movie suggestion

Recommender systems have become prevalent in recent years as they help users to access relevant items from the vast universe of possibilities available these days. Most existing research in this area is based purely on quantitative aspects such as indices of popularity or measures of similarity between items or users. This work introduces a novel perspective on movie recommendation that combines a basic quantitative method with a qualitative approach, resulting in a family of mixed character recommender systems. The proposed framework incorporates the use of arguments in favor or against recommendations to determine if a suggestion should be presented or not to a user. In order to accomplish this, Defeasible Logic Programming (DeLP) is adopted as the underlying formalism to model facts and rules about the recommendation domain and to compute the argumentation process. This approach has a number features that could be proven useful in recommendation settings. In particular, recommendations can account for several different aspects (e.g., the cast, the genre or the rating of a movie), considering them all together through a dialectical analysis. Moreover, the approach can stem for both content-based or collaborative filtering techniques, or mix them in any arbitrary way. Most importantly, explanations supporting each recommendation can be provided in a way that can be easily understood by the user, by means of the computed arguments. In this work the proposed approach is evaluated obtaining very positive results. This suggests a great opportunity to exploit the benefits of transparent explanations and justifications in recommendations, sometimes unrealized by quantitative methods.

A Proposal for Making Argumentation Computationally Capable of Handling Large Repositories of Uncertain Data

Data intensive applications with the capability of handling uncertain, imprecise, and inconsistent information are in constant demand. Efficient computational systems that can perform complicated inferences, obtain the appropriate conclusions, and explain the results are increasingly being required to act upon large databases. Argumentation systems could be used in the construction of interactive systems that are able to reason with large databases and/or different data sources. Notwithstanding, there are two important issues that need to be resolved in order to use argumentation in this kind of practical applications: adding the ability to deal with explicit uncertainty, and improving the computational complexity of argumentation, which so far has been an obstacle for its integration into interactive systems acting on large databases. In this paper we propose an argumentation-based system that has been engineered to address these issues.

A THEORETICAL FRAMEWORK FOR TRUST-BASED NEWS RECOMMENDER SYSTEMS AND ITS IMPLEMENTATION USING DEFEASIBLE ARGUMENTATION

Although the importance of trust in recommender systems is widely recognized, the actual mechanisms of trust propagation and trust preservation are poorly understood. This is partly due to the fact that trust is a complex notion, which is typically context dependent, subjective, dynamic and not always transitive or symmetrical. This paper presents a theoretical analysis of the notion of trust in news recommendation and discusses the advantages of modeling this notion using Defeasible Logic Programming, a general-purpose defeasible argumentation formalism based on logic programming. In the proposed framework, users can express explicit trust statements on news reports, news sources and other users. Trust is then modeled and propagated using a dialectical process supported by a Defeasible Logic Programming interpreter. A set of basic postulates for trust and their representation by means of defeasible rules is presented. The suitability of the approach is investigated with a set of illustrative examples and...

An argument-based multi-agent system for information integration

In this paper we address the problem of obtaining a consolidated view of the knowledge that a community of information agents possesses in the form of private, possibly large, databases. Each agent in the community has independent sources of information and each database could contain information that is potentially inconsistent and incomplete, both by itself and/or in conjunction with some of the others. These characteristics make the consolidation difficult by traditional means. The idea of obtaining a single view is to provide a way of querying the resulting knowledge in a skeptical manner, i.e., receiving one answer that reflects the perception of the information community. Agents using the proposed system will be able to access multiple sources of knowledge represented in the form of deductive databases as if they were accessing a single one. One application of this schema is a novel architecture for decision-support systems (DSS) that will combine database technologies, specifically federated databases, which we will cast as information agents, with an argumentation-based framework. Categories and Subjects Descriptors: I.2.4 [Artificial Intelligence]: Knowledge Representation Formalisms and Methods--Representation languages, Representations (procedural and rule-based); H.1.0 [Models and Principles]: Systems and Information Theory--General systems theory. General Terms: Algorithms, Design, Performance.

On the development of a formal methodology for knowledge representation in defeasible logic programming

Defeasible Logic Programming (DeLP) is a formalism able to represent incomplete and potentially contradictory information that combines logic programming with defeasible argumentation. In the past few years, this formalism has been applied to real world scenarios with encouraging results. Not withstanding, the outcome one may obtain in this or any other argumentative system is directly related to the decisions (or lack thereof) made during the phase of knowledge representation. In addition, this is exacerbated by the usual lack of a formal methodology able to assist the knowledge engineer during this critical phase. In this article, we propose a formal methodology for knowledge representation in DeLP, that defines a set of guidelines to be used during this phase. Our methodology results in an key tool to improve DeLPs applicability to concrete domains.