A solution of problems in multiagent systems involves representing beliefs of agents immersed in dynamic environments. Observation-based Defeasible Logic Programming (ODeLP) is an argument-based logic programming language that is used to represent an agent’s knowledge in the context of a…

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…

The notion of context and its importance in knowledge representation and nonmonotonic reasoning was first discussed in Artificial Intelligence by John McCarthy.
Ever since, contexts have found many applications in developing knowledge-based reasoning systems.
Defeasible argumentation has gained wide acceptance…

The problem of speeding up ínference has proved to be important in argumentative systems. When computing dialectical structures, several paths are searched (called argumentation lines), many of which will eventually prove useless. Morover, the performance of backward chainers degrades quickly…

In the restricted single-machine common due date problem the goal is to find a schedule for the n jobs which jointly minimizes the sum of earliness and tardiness penalties, while for the weighted tardiness problem the goal is to find…

Evolutionary algorithms (EAs) are merely blind search algorithms, which only make use of the relative fitness of solutions, but completely ignore the nature of the problem. Their performance can be improved by using new multirecombinative approaches, which provide a good…

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…

In this work we present an approach to model an action-oriented system controlled by BDI agents using a defeasible argumentation formalism to represent its knowledge. Here, our main concern will be modelling the software agents that drive the physical robots.

Knowledge acquisition for systems based on knowledge can be called knowledge engineering, according to Feigenbaum (1980) who used the term to describe the reduction of a wide context of knowledge for a set of facts and rules. The term Knowledge…

Effective document retrieval using domain knowledge and semantics is one of the major challenges in Information Retrieval. Over the last years, there has been a growing interest in ontologies as an artifact for human knowledge representation and a critical component…