Danilo Fum

Editorial: The cognitive modeling of human behavior: Why a model is (sometimes) better than 10,000 words

This special issue of Cognitive Systems Research presents a collection of remarkable papers on cognitive modeling based on communications delivered at ICCM-2006, the Seventh International Conference on Cognitive Modeling (Fum, Del Missier, & Stocco, 2006) held in Trieste, Italy, from April 5th to 8th, 2006. Being the organizers and chairmen of the conference, we have been invited to serve as guest editors for this issue. We therefore solicited some participants to reexamine their contributions, and to change them in form of journal articles. In particular, we asked authors to review what they had presented during the conference focusing on the benefits cognitive modeling could provide to cognitive science. The issue you are reading is the result of this editorial process. In this introductory commentary we would like to set the stage for what follows by illustrating the advantages and disadvantages of cognitive modeling, and by presenting a minimal set of requirements for a good modeling practice. Then, we will briefly preview the papers composing this special issue, and we will emphasize how they deal with the issues discussed in the previous sections.

Forward and backward reasoning in automatic abstracting

The paper is devoted to present a new approach to automatic abstracting which is supported by the development of SUSY, an experimental system currently being implemented at the University of Udine (Italy). The original contribution of the research reported is mostly focused on the role of forward and backward reasoning in the abstracting activity. In the paper the specifications and basic methodologies of SUSY are introduced, its architecture is illustrated with particular attention to the organization of the basic algorithms, and an example to support the novel approach proposed is described.

A distributed multi-agent architecture for natural language processing

The paper presents a distributed multi-agent architecture for natural language processing. This architecture proposes a novel concept of distributed problem solving, which incorporates in a unitary framework the following key-points; large-grained heterogeneous agents, centralized knowledge-based control, and mixed event-driven and goal-driven operation. It provides, moreover, a flexible tool for the design of natural language processing systems, both motivated from the cognitive point of view and computationally effective and robust. The proposed architecture has been implemented in a fully running prototype system, and has been successfully applied in the domain of text understanding.

The Use of Explanation-Based Learning for Modelling Student Behavior in Foreign Language Tutoring

An original methodology to model student performance which features a profitable integration of the bug collection and bug construction techniques is presented. This methodology has been used for building the modelling module of a new version of ET (English Tutor), an ITS aimed at supporting the learning of the English verb system. The proposed approach is based on the idea of analyzing the reasoning process of the student by reconstructing, step by step and in reverse order, the chain of reasoning (s)he has followed in giving his/her answer. Two kinds of errors, i.e., commission and omission errors, are considered by the the modeler and the student modelling process is supported by correct domain specific knowledge and by a catalogue of stereotyped errors (mal-rules). When the system is unable to explain the student behavior by exploiting its previous knowledge, new mal-rules are generated dynamically by utilizing explanation-based learning techniques. The overall process is based on a deep modelling of the student problem solving and the discrimination among possible explicative hypotheses about the student behavior is carried on non-monotonically through a truth maintenance system. The proposed approach has been fully implemented in a student modelling module developed in PROLOG.

Tense generation in an intelligent tutor for foreign language teaching: some issues in the design of the verb expert

The paper presents some of the results obtained within a research project aimed at developing ET (English Tutor), an intelligent tutoring system which supports Italian students in learning the English verbs. We concentrate on one of the most important modules of the system, the domain (i.e. verb) expert which is devoted to generate, in a cognitively transparent way, the right tense for the verb(s) appearing in the exercises presented to the student. An example which highlights the main capabilities of the verb expert is provided. A prototype version of ET has been fully implemented.

Naive vs. Formal Grammars: A Case for Integration in the Design of a Foreign Language Tutor

The relationship between formal and naive (i.e., used for didactic purposes) grammars in foreign language teaching is dealt with in the paper which presents, as a case study, an attempt to integrate the two approaches within an intelligent tutoring system. This work has been carried on in the framework of the ET (English Tutor) project whose long term goal is the development of a tutoring system aimed at helping Italian students master English verb tenses. Within the project, a prototype system based on a naive approach to the grammar of tense has been built. The experimentation performed with the prototype provided the motivation for a critical re-evaluation and revision of some of the assumptions which it was grounded upon. The possibility of integrating some naive intuitions into a systemic representation of grammatical knowledge is discussed in the paper, and a new version of the domain expert module exploiting the systemic approach to tense selection is illustrated.

Tailoring importance evaluation to reader's goals: a contribution to descriptive text summarization

The paper deals with a new approach to importance evaluation of descriptive texts developed in the framework of SUSY, an experimental system in the domain of text summarization. The problem of taking into account the readers goals in evaluating importance of different parts of a text is first analyzed. A solution to the design of a goal interpreter capable of computing a quantitative measure of the relevance degree of a piece of text according to a given goal is then proposed, and an example of goal interpreter operation is provided.

Text understanding with multiple knowledge sources: an experiment in distributed parsing

A novel approach to the problem of text understanding is presented, which exploits a distributed processing concept, where knowledge from different sources comes into play in the course of comprehension. In the paper the rationale of advocating such an approach and the advantages in following it are discussed. A prototype parser based on an original distributed problem-solving architecture is presented. It encompasses a centralized declarative control module and a collection of decentralized, loosely coupled, heterogeneous problem solvers specialized in the various facets of the parsing task. The mechanisms of coordination and communication among the specialists are illustrated, and an example of the parser operation is given. The parser is implemented in LISP on a SUN workstation.

Natural language processing and the automatic acquisition of knowledge: a simulative approach

The paper presents the general design and the first results of a research project whose long term goal is to develop and implement ALICE, an experimental system capable of augmenting its knowledge base by processing natural language texts. ALICE (an acronym for Automatic Learning and Inference Computerized Engine) is an attempt to model the cognitive processes that occur in humans when they learn a series of descriptive texts and reason about what they have learned. In the paper a general overview of the system is given with the description of its specifics, basic methodologies, and general architecture. How parsing is performed in ALICE is illustrated by following the analysis of a sample text.

A rule-based approach to evaluating importance in descriptive texts

Importance evaluation is one of the most challenging problems in the field of text processing. In the paper we focus on the notion of importance from a computational standpoint, and we propose a procedural, rule-based approach to importance evaluation. This novel approach is supported by a prototype experimental system, called importance evaluator, that can deal with descriptive texts taken from computer science literature on operating systems. The evaluator relies on a set of importance rules that are used to assign importance values to the different parts of a text and to resolve or explain conflicting evaluations. The system utilizes world knowledge on the subject domain contained in an encyclopedia and takes into account a goal assigned by the user for specifying the pragmatic aspects of the understanding activity. The paper describes the role of the evaluator in the frame of a larger system for text summarization (SUSY); it illustrates its overall mode of operation, and discusses some meaningful examples.