Juan Carlos Nieves

Preferred extensions as stable models

Given an argumentation framework AF, we introduce a mapping function that constructs a disjunctive logic program P, such that the preferred extensions of AF correspond to the stable models of P, after intersecting each stable model with the relevant atoms. The given mapping function is of polynomial size w.r.t. AF. In particular, we identify that there is a direct relationship between the minimal models of a propositional formula and the preferred extensions of an argumentation framework by working on representing the defeated arguments. Then we show how to infer the preferred extensions of an argumentation framework by using UNSAT algorithms and disjunctive stable model solvers. The relevance of this result is that we define a direct relationship between one of the most satisfactory argumentation semantics and one of the most successful approach of nonmonotonic reasoning i.e., logic programming with the stable model semantics.

Supporting decision making in urban wastewater systems using a knowledge-based approach

The use of knowledge-based systems has been shown to be a suitable approach to support decision making in environmental systems. Capturing and managing the huge quantity of data/information that has to be considered is an intrinsic factor that makes environmental systems a sophisticated domain. Organizing this data in a naive way can impact the efficacy of any knowledge-based system. Another intrinsic factor is the variety of data sources, which can result in inconsistent, uncertain or incomplete knowledge bases when different data sources are considered. Accordingly, two central issues of a successful knowledge-based system are the organization of its knowledge base and the expressiveness of its specification language. In this paper, we introduce a stratified framework for structuring any environmental knowledge base. We will argue that a declarative specification language, such as Answer Set Programming, is expressive enough to capture environmental knowledge bases that are inconsistent, uncertain and incomplete. We also present an automata-based approach to manage actions in knowledge-based systems. By solving a use case, specifically the diagnosis of the safety of a particular industrial wastewater discharge in an urban wastewater system, we illustrate how to represent relevant abstractions to model related complex processes. We show that by using them it is also possible to automate the diagnosis process (in the present case, for example, to diagnose problems at a wastewater treatment plant and afterward in the river) and hence support the decision-making task.

Semantics for possibilistic disjunctive programs

In this paper by considering answer set programming approach and some basic ideas from possibilistic logic, we introduce a possibilistic disjunctive logic programming approach able to deal with reasoning under uncertain and incomplete information. Our approach permits to use explicitly labels like possible, probable, plausible, etc., for capturing the incomplete state of a belief in a disjunctive logic program.

Pstable semantics for possibilistic logic programs

Uncertain information is present in many real applications e.g., medical domain, weather forecast, etc. The most common approaches for leading with this information are based on probability however some times; it is difficult to find suitable probabilities about some events. In this paper, we present a possibilistic logic programming approach which is based on possibilistic logic and PStable semantics. Possibilistic logic is a logic of uncertainty tailored for reasoning under incomplete evidence and Pstable Semantics is a solid semantics which emerges from the fusion of non-monotonic reasoning and logic programming; moreover it is able to express answer set semantics, and has strong connections with paraconsistent logics.

A Schema for Generating Relevant Logic Programming Semantics and its Applications in Argumentation Theory

In the literature, there are several approaches which try to perform common sense reasoning. Among them, the approaches which have probably received the most attention the last two decades are the approaches based on logic programming semantics with negation as failure and argumentation theory. Even though both approaches have their own features, it seems that they share some common behaviours which can be studied by considering the close relationship between logic programming semantics and extension-based argumentation semantics. In this paper, we will present a general recursive schema for defining new logic programming semantics. This schema takes as input any basic logic programming semantics, such as the stable model semantics, and gives as output a new logic programming semantics which satisfies some desired properties such as relevance and the existence of the intended models for every normal program. We will see that these new logic programming semantics can define candidate extension-based argumentation semantics. These new argumentation semantics will overcome some of the weakness of the extension-based argumentation semantics based on admissible sets. In fact, we will see that some of these new argumentation semantics have similar behaviour to the extension-based argumentation semantics built in terms of strongly connected components.

Possibilistic semantics for logic programs with ordered disjunction

Logic programs with ordered disjunction (or LPODs) have shown to be a flexible specification language able to model and reason about preferences in a natural way. However, in some realistic applications which use user preferences in the reasoning, information can be pervaded with vagueness and a preference-aware reasoning process that can handle uncertainty is required. In this paper we address these issues, and we propose a framework which combines LPODs and possibilistic logic to be able to deal with a reasoning process that is preference-aware, non-monotonic, and uncertain. We define a possibilistic semantics for capturing logic programs with possibilistic ordered disjunction (or LPPODs) which is a generalization of the original semantics. Moreover, we present several transformation rules which can be used to optimize LPODs and LPPODs code and we show how the semantics of LPODs and the possibilistic semantics of LPPODs are invariant w.r.t. these transformations.

Possibilistic-Based Argumentation: An Answer Set Programming Approach

In many fields of automated information processing it becomes crucial to consider together imprecise, uncertain or inconsistent information. Argumentation theory is a suitable framework for practical and uncertain reasoning, where arguments could support conclusions. We present a possibilistic-based argumentation approach which is based on a possibilistic disjunctive language. This specification language is able to capture incomplete information and incomplete states of a knowledge base at the same time.

Expressing Extension-Based Semantics Based on Stratified Minimal Models

Extension-based argumentation semantics is a successful approach for performing non-monotonic reasoning based on argumentation theory. An interesting property of some extension-based argumentation semantics is that these semantics can be characterized in terms of logic programming semantics. In this paper, we present novel results in this topic. In particular, we show that one can induce an argumentation semantics (that we call Stratified Argumentation Semantics) based on a logic programming semantics that is based on stratified minimal models. We show that the stratified argumentation semantics overcome some problems of extension-based argumentation semantics based on admissible sets and we show that it coincides with the argumentation semantics CF2.

Coordination, Organisation and Model-driven Approaches for Dynamic, Flexible, Robust Software and Services Engineering

Enterprise systems are increasingly composed of (and even functioning as) components in a dynamic, digital ecosystem. On the one hand, this new situation requires flexible, spontaneous and opportunistic collaboration activities to be identified and established among (electronic) business parties. On the other, it demands engineering methods that are able to integrate new functionalities and behaviours into running systems composed by active, distributed, interdependent processes. Here we present a multi-level architecture that combines organisational and coordination theories with model driven development, for the implementation, deployment and management of dynamic, flexible and robust service-oriented business applications, combined with a service layer that accommodates semantic service description, fine-grained semantic service discovery and the dynamic adaptation of services to meet changing circumstances.

A Possibilistic Argumentation Decision Making Framework with Default Reasoning

In this paper, we introduce a possibilistic argumentation-based decision making framework which is able to capture uncertain information and exceptions/defaults. In particular, we define the concept of a possibilistic decision making framework which is based on a possibilistic default theory, a set of decisions and a set of prioritized goals. This set of goals captures user preferences related to the achievement of a particular state in a decision making problem. By considering the inference of the possibilistic well-founded semantics, the concept of argument with respect to a decision is defined. This argument captures the feasibility of reaching a goal by applying a decision in a given context. The inference in the argumentation decision making framework is based on basic argumentation semantics. Since some basic argumentation semantics can infer more than one possible scenario of a possibilistic decision making problem, we define some criteria for selecting potential solutions of the problem.