Afzal Ballim

Artificial Believers: The Ascription of Belief

Contents: Introduction. Preliminaries on the Nature of Belief. Belief Ascription. Experiments in Belief. Global Issues: Reasoning with Viewpoints. Further Extensions and Speculations.

Beliefs, stereotypes and dynamic agent modeling

In many domains (such as dialogue participation and multi-agent cooperative planning) it is often necessary that the system maintains complex models of the beliefe of agents with whom it is interacting. In particular, it is normally the case that models of the beliefs of agents about another agents beliefs must be modeled. While in limited domains it is possible to have such nested belief models pregenerated, in general it is more reasonable to have a mechanism for generating the nested models on demand. Two methods for such generation are discussed, one based on triggering stereotypes, and the other based on perturbation of the systems beliefs. Both of these approaches have limitations. An alternative is proposed that merges the two approaches, thus gaining the benefits of each and using those benefits to avoid the problems of either of the individual methods.

A practical approach to multiple default inheritance for unification-based lexicons

This paper describes a unification-based lexicon system for NLP applications that incorporates mechanisms for multiple default inheritance. Such systems are intractable in the general case---the approach adopted here places a number of restrictions on the inheritance hierarchy in order to remove some of the sources of complexity while retaining more desirable properties. Implications of the design choices are discussed, comparisons are drawn with related work in computational linguistics and AI, and illustrative examples from the lexicons of German and English are given.

Belief Ascription, Metaphor, and Intensional Identification

This article discusses the extension of ViewGen, an algorithm derived for belief ascription, to the areas of intensional object identification and metaphor. ViewGen represents the beliefs of agents as explicit, partitioned proposition sets known as environments. Environments are convenient, even essential, for addressing important pragmatic issues of reasoning. The article concentrates on showing that the transfer of informotion in metophors, intensional object identification, and ordinary, nonmetaphorical belief ascription can all be seen OS different manifestations of a single environment-amalgomatian process. The article also briefly discusses the extension of ViewGen to speech-act processing and the addition of a heuristic-based, relevance-determination procedure. and justifies the partitioning approach to belief ascription.

LHIP: extended DCGs for configurable robust parsing

We present LHIP, a system for incremental grammar development using an extended DCG for malism. The system uses a robust island-based parsing method controlled by user-defined performance thresholds.

A language for the statement of binary relations over feature structures

Unification is often the appropriate method for expressing relations between representations in the form of feature structures; however, there are circumstances in which a different approach is desirable. A declarative formalism is presented which permits direct mappings of one feature structure into another, and illustrative examples are given of its application to areas of current interest.

Propositional attitude framework requirements

Abstract The purpose of this paper is to focus attention on various important aspects and problems of propositional attitude representation in the hope of engendering fruitful discussion on these topics, and in particular in the hope that such discussion might lead to a determination of useful criteria for evaluating the merits and drawbacks of individual systems. As such, the paper does not purport to offer ‘the solutions’ to the mentioned problems, nor are the problems discussed here intended to be an exhaustive catalogue of the problems that must be solved by successful systems. The problems are, however, advanced as a useful set of criteria for evaluating systems by considering which problems are tackled, and how they are tackled.

Belief Systems: Ascribing Belief

In this decade, it has been realised that belief Systems are an important part of any artificial intelligence (AI) System that interacts with individuals. For correct, and maximal behaviour it is necessary to account for the beliefs of other individuals that differ from the systems. This paper considers the work of a number of researchers on representing and reasoning with belief. Further, it describes our own work on the automatic ascription of belief (i.e, the generation of nested beliefs on demand). This ascription is accomplished by a form of default reasoning. Particular attention is paid to beliefs that are not ascribed by default (known as atypical beliefs.)

Agent-Oriented Language Engineering for Robust NLP

The main goal of our work is to propose an agent-oriented framework for developing robust NLP applications. This framework provides means to compose analysis modules in a co-operative style. The idea is to encapsulate existing analysis tools and resources within software agents coordinated at a higher level using meta-knowledge. Agents can be activated concurrently and they should provide their linguistic competence depending on the application needs. The activation policy is determined by the context, by the domain knowledge and by performance constraints. At this level, co-ordination is computational logic-based in order to exploit known inference mechanisms for the decision support. This framework should be general enough to cope with other kinds of information sources, such as multimedia documents and with multimodal dialogue systems.

New Mexico State University: Computing Research Laboratory

CRLs contribution to DARPAs program is to bring to bear on natural language understanding two closely-related belief and context mechanisms: dynamic generation of nested belief structures (ViewGen) and hypotheses for reasoning and problem-solving (MGR).