On the effect of dynamic environments in defeasible reasoning

The design of intelligent agents has become a key issue for many interesting applications. Given that there is no universally accepted definition of intelligence, Russell developed the notion of rational agency as an alternative for the characterization of intelligent agency [11]. In short, an agent is said to be rational if it performs the right actions according to the information it possesses and the goals it wants to achieve.Eje: Inteligencia Artificial Distribuida, Aspectos Teóricos de la Inteligencia Artificial y Teoría de la ComputaciónRed de Universidades con Carreras en Informática (RedUNCI

Defeasible reasoning in dynamic domains

The design of intelligent agents is a key issue for many applications. Since there is no universally accepted definition of intelligence, the notion of rational agency was proposed by Russell as an alternative for the characterization of intelligent agency. A rational agent must have models of itself and its surroundings to use them in its reasoning. To this end, this paper develops a formalism appropriate to represent the knowledge of an agent. Moreover, if dynamic environments are considered, the agent should be able to observe the changes in the world, and integrate them into its existing beliefs. This motivates the incorporation of perception capabilities into our framework. The abilities to perceive and act, essential activities in a practica! agent, demand a timely interaction with the environment. Given that the cognitive process of a rational agent is complex and computationally expensive, this interaction may not be easy to achieve. To solve this problem, we propase inference mechanisms that rely on the use precompiled knowledge to optimize the reasoning process.I Workshop de Agentes y Sistemas Inteligentes (WASI)Red de Universidades con Carreras en Informática (RedUNCI

Using logic programs to model an agent's epistemic state

The notion of rational agency was proposed by Russell [9] as an alternative characterization of intelligence agency. Loosely speaking, an agent is said to be rational if it perfomns the right actions according to the information it possesses and the goals it wants to achieve. Unfortunately, the enterprise of constructing a rational agent is a rather complex task. Although in the last few years there has been an intense flowering of interest in the subject, it is still in its early beginnings: several issues remain overlooked or addressed under too unrealistic assumptions. As slated by Pollock. [5], a rational agent should have models of itself and its surroundings, since it must be able to draw conclusions from this knowledge that compose its set of beliefs. Traditional approaches rely on multi-modal logics to represent the agent's epistemic state [7. l]. Given the expressive power of these formalisms, their use yields proper theoretical models. Nevertheless, the advantages of these specifications lend to be lost in the transition towards practical systems: there is a tenuous relation between the implementations based on these logics and their theoretical foundations [8]. Modal logics systems suffer from a number of drawbacks, notably the well-known logical omniscience problem [10]. This problem arises as a by-product of the necessitation rule and the K axiom, present in any normal modal system. Together, these ruIes imply two unrealistic conditions: an agent using this system must know all the valid formulas, and its beliefs should be closed under logical consecuence. These properties are overstrong for a resource-bounded reasoner lo achieve them. Therefore, the traaditional modal logic approach is not suitable for representing practical believers [11]. We intend to use logic programs as an alternative representation for the agent's epistemic state. This formalization avoids the aforementioned problems of modal logics, and admits a seamless transition between theory and practice. In the next section we detail our model and highlight its advantages. Next, sectiol1 3 prescnts sume conclusions and reports on the forthcoming work.Eje: Aspectos teóricos de inteligencia artificialRed de Universidades con Carreras en Informática (RedUNCI

An argumentation framework with uncertainty management designed for dynamic environments

Nowadays, data intensive applications are in constant demand and there is need of computing environments with better intelligent capabilities than those present in today's Database Management Systems (DBMS). To build such systems we need formalisms that can perform complicate inferences, obtain the appropriate conclusions, and explain the results. Research in argumentation could provide results in this direction, providing means to build interactive systems able to reason with large databases and/or di erent data sources. In this paper we propose an argumentation system able to deal with explicit uncertainty, a vital capability in modern applications. We have also provided the system with the ability to seamlessly incorporate uncertain and/or contradictory information into its knowledge base, using a modular upgrading and revision procedurePresentado en el X Workshop Agentes y Sistemas InteligentesRed de Universidades con Carreras en Informática (RedUNCI

Characterizing defeat in observation-based defeasible logic programming

In this work we analyze the problem of incorporating specificity to characterize defeat in a particular argumentative framework, called observation based defeasible logic programming (ODeLP) [1]. Eficiency is an important issues in ODeLP, since this framework has been de ned for representing the knowledge of intelligent agents in real world applications. Computing specificity using domain knowledge is a demanding operation. Thus, have devised a new version of this criterion, that optimizes the computation of the defeat relation.Eje: Inteligencia artificial distribuida, aspectos teóricos de la inteligencia artificial y teoría de computaciónRed de Universidades con Carreras en Informática (RedUNCI

On cumulativity in the context of defeasible argumentation

Las lógicas que permiten razonar de manera no-monótona suelen ser caracterizadas por la propiedad que carecen - casualmente, la monotonía - en vez de serlo por aquellas que sí gozan. Gabbay, Makinson y Kraus propusieron un conjunto de propiedades básicas de las relaciones de inferencia que toda teoría no-monótona debería satisfacer. No obstante, existen varios formalismos aparentemente razonables que no satisfacen algunos de estos principios, por caso la mayoría de los formalismos de argumentación rebatible. En este artículo determinamos el estado de estas propiedades básicas en el marco de dos populares sistemas argumentativosLogics for nonmonotonic reasoning have often been described by the property they lack—that is, monotonicity—instead of by those they do enjoy. Gabbay, Makinson and Kraus proposed a set of core properties for inference relations that every nonmonotonic theory ought to have. Yet, there are some apparently well-behaved formalisms that fail to comply with some of these principles, such as most defeasible argumentation formalisms. In this article we determine the status of these core properties in the context of two well-known argumentation frameworks.Workshop de Agentes y Sistemas Inteligentes (WASI)Red de Universidades con Carreras en Informática (RedUNCI

Introducing dialectical bases in defeasible argumentation

Defeasible argumentation is a form of defeasible reasoning, that emphasizes the notion of an argument. An argument A for a conclusion q is a tentative piece of reasoning which supports q. In an argumentative framework, common sense reasoning can be modeled as a process in which we must determine whether an argument justifies its conclusion. The process mentioned aboye takes considerable computational effort. For this reason it would be convenient to keep a repository of already computed justifications to save work already done with previously solved queries. In this paper we introduce the concept of dialectical bases as a first step in direction to defining a justification maintenance system for argumentative frameworks.Eje: Aspectos teóricos de la inteligencia artificialRed de Universidades con Carreras en Informática (RedUNCI

Building precompiled knowledge in ODeLP

Argumentation systems have substantially evolved in the past few years, resulting in adequate tools to model some forms of common sense reasoning. This has sprung a new set of argument-based applications in diverse areas. In previous work, we defined how to use precompiled knowledge to obtain significant speed-ups in the inference process of an argument-based system. This development is based on a logic programming system with an argumentation-driven inference engine, called Observation Based Defeasible Logic Programming (ODeLP). In this setting was first presented the concept of dialectical databases, that is, data structures for storing precompiled knowledge. These structures provide precompiled information about inferences and can be used to speed up the inference process, as TMS do in general problem solvers. In this work, we present detailed algorithms for the creation of dialectical databases in ODeLP and analyze these algorithms in terms of their computational complexity.Red de Universidades con Carreras en Informática (RedUNCI

On the effect of dynamic environments in defeasible reasoning

The design of intelligent agents has become a key issue for many interesting applications. Given that there is no universally accepted definition of intelligence, Russell developed the notion of rational agency as an alternative for the characterization of intelligent agency [11]. In short, an agent is said to be rational if it performs the right actions according to the information it possesses and the goals it wants to achieve.Eje: Inteligencia Artificial Distribuida, Aspectos Teóricos de la Inteligencia Artificial y Teoría de la ComputaciónRed de Universidades con Carreras en Informática (RedUNCI

Analyzing the defeat relation in observation-based defeasible logic programming

In the last decade several ways to formalize defeasible reasoning have been studied. A particular approach, defeasible argumentation, has been particularly successful to achieve this goal. The inference process of argument-based systems is based on the interaction of ar- guments for and against certain conclusions. The relations of attack and defeat among arguments are key elements in these inference process. Usually a preference criterion is used to calculate the defeat relation to decide, in case of con ict, which argument is preferred over its contender. Speci city is a domain independent principle that has been used in several formalisms. In this work we analyze the problem of incorporating speci city to characterize defeat in a particular argumentative framework, called Observation Based Defeasible Logic Program- ming. Since e ciency is an important issue in ODeLP, we have devised a new version of this criterion, that optimizes the computation of the defeat relation. We also present a formal proof to show that this new version is equivalent to the old one.Eje: V - Workshop de agentes y sistemas inteligentesRed de Universidades con Carreras en Informática (RedUNCI