In a multi-agent environment, where self-motivated agents try to pursue their own goals, cooperation cannot be taken for granted. Cooperation must be planned for and achieved through communication and negotiation. We present a logical model of the mental states of the agents based on a representation of their beliefs, desires, intentions, and goals. We present argumentation as an iterative process emerging from exchanges among agents to persuade each other and bring about a change in intentions. We look at argumentation as a mechanism for achieving cooperation and agreements. Using categories identified from human multi-agent negotiation, we demonstrate how the logic can be used to specify argument formulation and evaluation. We also illustrate how the developed logic can be used to describe different types of agents. Furthermore, we present a general Automated Negotiation Agent which we implemented, based on the logical model. Using this system, a user can analyze and explore differe...

Revising beliefs is a task any intelligent agent has to perform. For this reason, belief revision has received much interest in Artificial Intelligence. However, there are serious problems when trying to analyze belief revision techniques developed in the field of Artificial Intelligence on the knowledge level. The symbolic representation of beliefs seems to be crucial. The theory of epistemic change shows that a partial knowledge-level analysis of belief revision is possible, but leaves open the question of how this theory is related to belief revision approaches in Artificial Intelligence. In particular, it remains an open question whether the results achieved in the knowledge-level analysis are valid. Furthermore, the idea of reason maintenance, which is considered to be essential in AI, has no counter-part in the theory of epistemic change. Addressing these problems, it is shown how to reconstruct symbol-level belief revision on the knowledge level. 1 Introduction Any intelligen...

this article.

Existing conflict detection methods for CSP's such as [de Kleer, 1989; Ginsberg, 1993] cannot make use of powerful propagation which makes them unusable for complex real-world problems. On the other hand, powerful constraint propagation methods lack the ability to extract dependencies or conflicts, which makes them unusable for many advanced AI reasoning methods that require conflicts, as well as for interactive applications that require explanations. In this paper, we present a non-intrusive conflict detection algorithm called QUICKXPLAIN that tackles those problems. It can be applied to any propagation or inference algorithm as powerful as it may be. Our algorithm improves the efficiency of direct non-intrusive conflict detectors by recursively partitioning the problem into subproblems of half the size and by immediately skipping those subproblems that do not contain an element of the conflict. QUICKXPLAIN is used as explanation component of an advanced industrial constraint-based configuration tool.

The use of logic for knowledge representation and reasoning systems is controversial. There are, indeed, several ways that standard First Order Predicate Logic is inappropriate for modelling natural language understanding and commonsense reasoning. However, a more appropriate logic can be designed. This chapter presents several aspects of such a logic.

Systems of defeasible reasoning are characterized by defeasible proofs, called arguments. I claim that sensible criteria of defeat among arguments in those systems are feasible, be it to a certain extent. As defeat eventually becomes unenforceable, the only option left is to pursue both arguments concurrently. This paper tries to confine the reach of defeat among arguments by means of several case studies, some of which are taken from the literature. 1. INTRODUCTION Reasoning beyond the information enclosed in the premises is a tempting but risky activity. It is tempting, because sheer deductive reasoning brings us no more than what was already recorded in the premises. And it is risky, because we might jump to the wrong conclusions. This is, very briefly, the issue of ampliative inference mechanisms. Ampliative inference can be defined as the result of rational non-deterministic non-monotonic reasoning (Loui, 1990). The term itself is suggested by the American philosopher Peirce (183...

As part of an interdisciplinary project to develop a computational cognitive model of a reader of narrative text, we are developing a computational theory of how natural-language-understanding systems can automatically acquire new vocabulary by determining from context the meaning of words that are unknown, misunderstood, or used in a new sense. `Context' includes surrounding text, grammatical information, and background knowledge, but no external sources. Our thesis is that the meaning of such a word can be determined from context, can be revised upon further encounters with the word, "converges" to a dictionary-like definition if enough context has been provided and there have been enough exposures to the word, and eventually "settles down" to a "steady state" that is always subject to revision upon further encounters with the word. The system is being implemented in the SNePS knowledgerepresentation and reasoning system. This essay is forthcoming as a chapter in Iwanska, L/ucja, & S...

Theories of rational belief revision recently proposed by Alchourr'on, Gardenfors, Makinson, and Nebel illuminate many important issues but impose unnecessarily strong standards for correct revisions and make strong assumptions about what information is available to guide revisions. We reconstruct these theories according to an economic standard of rationality in which preferences are used to select among alternative possible revisions. By permitting multiple partial specifications of preferences in ways closely related to preference-based nonmonotonic logics, the reconstructed theory employs information closer to that available in practice and offers more flexible ways of selecting revisions. We formally compare this new conception of rational belief revision with the original theories, adapt results about universal default theories to prove that there is unlikely to be any universal method of rational belief revision, and examine formally how different limitations on rationality affe...

Natural language competent embodied cognitive agents should satisfy two requirements. First, they should act in and reason about a changing world, using reasoning in the service of acting and acting in the service of reasoning. Second, they should be able to communicate their beliefs, and report their past, ongoing, and future actions in natural language. This requires a representation of time using a deictic NOW, that models the compositional semantic properties of the English "now". Two problems emerge for an agent that interleaves reasoning and acting in a personal time. The first concerns the representation of plans and reactive rules involving reasoning about "future NOWs". The second emerges when, in the course of reasoning about NOW, the reasoning process itself results in NOW changing. We propose solutions for the two problems and conclude that: (i) for embodied cognitive agents, time is not just the object of reasoning, but is embedded in the reasoning pr...

: This paper considers student modelling from the point of view of the formal techniques that are involved. It attempts to provide a theoretical, computational basis for student modelling which is psychologically neutral and independent of applications. It is derived mainly from various areas of theoretical artificial intelligence. Because of the intrinsic difficulty of the student modelling problem, these links to AI are often merely pointed out and not pursued in depth. Contents 1. Introduction 2. Foundations 3. An example 4. Initialising the student model 4.1 Explicit questioning 4.2 Default assumptions 5. Updating the student model 5.1 Diagnosis 5.1.1 Reconstruction 5.1.2 Cognitive diagnosis 5.1.3 Generative mechanisms 5.2 Revising beliefs 5.2.1 Discarding beliefs 5.2.2 Creating beliefs through reasoning 5.2.3 Limited reasoning 5.2.4 Meta-reasoning 5.2.5 Non-monotonic reasoning 5.2.6 Creating beliefs through learning 5.3 Beyond belief 5.3.1 Belief structures 5.3.2 Viewpoints 5.3....