Ontology schema for an agent belief store

2001

The research group at CENTRIA UNL in Lisbon, in collaboration with Ferrara and Linköping, has been quite active in the field of Logic Programming (LP) geared towards rational agents over several years, and has investigated a spate of rational agent functionalities and their implementations. In particular, the Lisbon group has been active in the fields of learning, abduction, updating, argumentation, paraconsistency, belief revision, soft LP, contradicition removal, diagnosis, and debugging. These research directions are all included in the broader long term research avenue of building evolving and collaborative rational epistemic agents, whose overarching objective is to establish a flexible declarative language for the specification and implementation of dynamic knowledge construction in a society of agents. In [32, 33], the philosophical foundations and the general line of approach to the definition of rational agents, based on and building upon the strengths of LP, have been put ...

2012

Web Ontology Language (OWL) provides means to semantically represent domain knowledge as ontologies. Then, ontological reasoning allows software agents to effectively share and semantically interpret the knowledge. OWL adopts open world semantics and in order to achieve decidability its expressiveness is strictly limited. Therefore, many real-life problems cannot be represented only using ontologies and cannot be solved using just ontological reasoning.

2013

Combining knowledge and beliefs of autonomous peers in distributed settings, is a major challenge. In this talk we consider agents that combine their ontologies and reason jointly with their coupled knowledge using the E-SHIQ representation framework. We motivate the need for a representation framework that allows agents to combine their knowledge in different ways, maintaining the subjectivity of their own knowledge and beliefs, and to reason collaboratively, constructing a tableau that is distributed among them. The talk presents the E−SHIQ representation framework and the tableau reasoning algorithm. It presents the implications to the modularization of ontologies for efficient reasoning, implications to coordinating agents’ subjective beliefs, as well as challenges for reasoning with ontologies in open and dynamic multi-agent systems. 1 Combining Ontologies with E − SHIQ To combine knowledge and beliefs of autonomous agents in open and inherently distributed settings, we need sp...

Computational Intelligence, 1995

The epistemic notions of knowledge and belief have most commonly been modeled by means of possible worlds semantics. In such approaches an agent knows (or believes) all logical consequences of its beliefs. Consequently, several approaches have been proposed to model systems of explicit belief, more suited to modeling finite agents or computers. In this paper a general framework is developed for the specification of logics of explicit belief. A generalization of possible worlds, called situations, is adopted. However the notion of an accessibility relation is not employed; instead a sentence is believed if the explicit proposition expressed by the sentence appears among a set of propositions associated with an agent at a situation. Since explicit propositions may be taken as corresponding to "belief contexts" or "frames of mind," the framework also provides a setting for investigating such approaches to belief. The approach provides a uniform and flexible basis from which various issues of explicit belief may be addressed and from which systems may be contrasted and compared. A family of logics is developed using this framework, which extends previous approaches and addresses issues raised by these earlier approaches. The more interesting of these logics are tractable, in that determining if a belief follows from a set of beliefs, given certain assumptions, can be accomplished in polynomial time.

1995

Abstract Building ontology is one of key issues for knowledge sharing which is important technology to realize largescale knowledge-base systems. In this paper, we propose a computational framework for constructive ontologies called aspect. We first formalize aspect in a logical framework and then define it as a programming language.

2000

A formal language to describe mental states of rational agents is useful to specify the behavior of arti cial agents that are supposed to act intelligently and to help human beings in evaluating their reasoning about the external environment. This paper presents a multi-agent, propositional, temporal logic to describe the mental state of rational agents. The formal properties of beliefs and intentions of rational agents and the relationships between such mental attitudes are rst informally sketched. Then, a logical language whose semantics captures these properties and relationships is presented. In particular, this logic provides two modal operators to describe rational agents' beliefs and intentions at each time instant. The above-mentioned properties of beliefs and intentions are formalized as logical axioms.

2011

Artificial agents functioning in the Semantic Web are to be capable of getting knowledge from diverse sources. This implies the capability to continuously update their knowledge bases. New stream reasoning concepts make this need even more pressing. Semantic Web ontologies are commonly represented using description logic knowledge bases. We propose an agent architecture with such features, utilizing a Dynamic Reasoning System (DRS). This explicitly portrays reasoning as a process taking place in time and allows for manipulating inconsistent knowledge bases. We sketch a procedure for user-directed ontology debugging. This same mechanism can be used for automated belief revision. We identify important research directions that may benefit from this approach.

2005

This paper is an overview of the work that we have carried on in the last two years in the context of the MASSiVE project. The main research lines have concerned personalization of the interaction with web services, personalization of courseware, web services interoperability, and integrated environments for agent oriented software engineering. All of them can be seen as applications of different reasoning techniques to a declarative specification of interaction. A declarative specification makes the study of properties easy and allows a fast prototyping of applications. In particular, we applied reasoning about actions and change to the personalized selection and composition of web services and to the construction of courseware that satisfies the user's needs and goals. This kind of reasoning has also been integrated in the DCaseLP MAS prototyping environment. Declarative specifications have also been helpful to face the problem of proving policy conformance in a way that guarantees web service interoperability. Finally, the adoption of process languages for web services for expressing the procedural behavior of adaptive BDI-style agents have been explored.

The current paper details a restricted semantics for active logic, a time-sensitive, contradictiontolerant logical reasoning formalism. Central to active logic are special rules controlling the inheritance of beliefs in general, and beliefs about the current time in particular, very tight controls on what can be derived from direct contradictions (P &¬P ), and mechanisms allowing an agent to represent and reason about its own beliefs and past reasoning. Using these ideas, we introduce a new definition of model and of logical consequence, as well as a new definition of soundness such that, when reasoning with consistent premises, all classically sound rules are sound for active logic. However, not everything that is classically sound remains sound in our sense, for by classical definitions, all rules with contradictory premises are vacuously sound, whereas in active logic not everything follows from a contradiction.

As suggested in the literature, revising and updating beliefs and knowledge bases is an important unsolved topic in knowledge representation and reasoning that requires a solid theoretical basis, particularly in current applications of Artificial Intelligence where an agent can work in an open dynamic environment with incomplete information. Various researchers have combined postulates and Answer Set Programming as key components to set up their approaches. However, many of such proposals still present some shortcomings when dealing with persistence situations, redundant information, contradictions or lack of further properties of knowledge evolution. In need to satisfy more general principles and suggesting a frame of reference, this paper consists of a study of consistency preservation and restoration, and a new AGM-characterisation of a semantics for updates of logic programs. It consists in performing updates of epistemic states that meets well-accepted belief revision postulates. Besides the set of properties that this framework shares with other equivalent update semantics, this proposal is also supported by a solver prototype as an important component of logic programming and automatic testbed of its declarative version. The existence of a solver for a theoretical framework helps to automatically compute agents' knowledge bases for more complex prototypes and frameworks.