Francisco Cervantes-Pérez

A neural model of interactions subserving prey-predator discrimination and size preference in anuran amphibia.

The model described is an extension of a previous model of the optic tectum (Arbib & Lara, 1982; Lara, Arbib & Cromarty, 1982; Lara & Arbib, 1982) and takes into consideration anatomical, physiological and behavioral studies in anurans, as well as earlier modelling efforts (Ewert & Von Seelen, 1974; Didday, 1976). Computer simulations were conducted to analyze how interactions among retina, optic tectum and pretectum may give frogs and toads the ability to discriminate between prey and predator stimuli. Results from simulations have allowed us to reproduce empirical observations, to suggest new experiments, and to postulate what neural mechanisms might be involved in some phenomena related to prey-catching orienting behavior, with direction invariance of prey-predator recognition being a consequence of tectal architecture, and size preference and response latency depending on the motivational state of the animal.

Predictive student model supported by fuzzy-causal knowledge and inference

In this article we explore the paradigm of student-centered education. The aim is to enhance the learning of students by the self-adaptation of a Web-based educational system (WBES). The adaptive systems behavior is achieved as a result of the decisions made by a student model (SM). The decision reveals the lecture option most suitable to teach a concept according to the students profile. Thus, the lecture content is authored from different view points (e.g. learning theory, type of media, complexity level, and user-system interaction degree). The purpose is to tailor several educational options to teach a given concept. Thereby, the SM elicits psychological attributes of the student to describe subjective traits, such as: cognitive, personality, and learning preferences. It also depicts pedagogical properties of the available lectures options. Moreover, the SM dynamically builds a cognitive map (CM) to set fuzzy-causal relationships among the lectures option properties and the students attributes. Based on a fuzzy-causal engine, the SM predicts the bias that a lectures option exerts on the students apprenticeship. The conceptual, theoretical, and formal grounds of the approach were tested by a computer implementation of the SM and an experiment. As a result of a field trial, we found that: the average learning acquired by an experimental group of volunteers that used this approach was 17% higher than the average apprenticeship of another equivalent control group, whose lectures were randomly chosen. Thus we conclude that: learning is better stimulated when the delivered lectures account a students profile than when they ignore it.

Modeling the Strategic Process of Decision-Making Support Systems Implementations: A System Dynamics Approach Review

Implementing decision-making support systems (DMSS) is considered an organizationally complex and risky task that is influenced by dynamic technical and social-political issues. Consequently, DMSS implementation failures, with associated economic loses, are still reported. While several statistics-based (static) quantitative models of successful factors and qualitative (descriptive) models to implement DMSS are available, few quantitative dynamic models have been posed. In this paper, we illustrate how a dynamic simulation model of the DMSS implementation process can be designed. We use a system dynamics approach via an extended methodology, which is called critical realism-based methodology for studying soft systems dynamics. Validation is realized through 1) the theoretical validity of the model, 2) the models capability in reproducing historical DMSS implementation paths, and 3) the models capability in predicting new DMSS implementation paths from new cases. Simulation results suggest the adequacy of using these modeling methods to complement the knowledge on the DMSS implementation processes.

Modulatory effects on Prey-Recognition in Amphibia: A Theoretical-Experimental study

In this paper, we follow an hibrid approach —a theory- experiment cycle— for the study of visuomotor coordination in amphibians (e.g., toads). We use a neural net model of the reti-no-tectal-pretectal interactions to analyze, via computer simulations and mathematical analyses, how its activation dynamics may underly prey-catching behavior. In particular, we study how changes in the hypothesized pretectal inhibitory effect over tectum might account for the modulation of prey-like stimuli discrimination. Our analysis shows, at the tectal level, that the efficacy of the stimulus characteristics (e.g., form and velocity) to produce the proper discrimination between different stimuli (e.g., worm-like and square) depends on the intensity and temporal characteristics of pretectal activation. Then, we explored this prediction experimentally, in toad Bufo marinus horribilis, by changing some of the motivational factors that affect toad’s response frequency to prey-like stimuli. Animals under different maintenance conditions in the laboratory (i.e., relationship between feeding and stimulation time) were stimulated with worm- like and square stimuli moving at different velocities. We found that the stimulus characteristics might become irrelevant to produce optimal prey-catching behavior. These combined, mathematical and experimental, analyses allow us to postulate that toad’s motivational state might modulate the intensity and temporal characteristics of pretectal activation.

Schema Theory as a Common Language to Study Sensori-Motor Coordination

Among sciences there is a tendency to generate data and knowledge in one discipline without making it available to other disciplines. We discuss how Arbib’s Schema Theory represents an attempt to solve this isolation problem in Cognitive Science by providing us with a global language explaining cognitive processes at a level that can be used, and understood, within all disciplines of cognitive sciences. In addition, we show how Schema Theory adds a key methodology to the “top-down” approach which allows us to set the stage within the theory-experiment cycle in order to investigate the neural substrate of sensori-motor coordination. We use our analyses of visuomotor coordinations in toads and praying mantises as examples (i) to show the applicability of Schema Theory in the study of what processes should occur within an animal’s brain in order to explain overall behaviors, and (ii) to point out how Schema Theory permits, in a very general way, to work top-down. The ultimate aim is to generate testable hypotheses about the neural mechanisms that underlie behavior.

IDSSE-M: Intelligent Decision Support Systems Engineering Methodology

This chapter describes and illustrates IDSSE-M, a methodology for designing and building intelligent decision support systems. IDSSE-M follows a prototype-based evolutive approach on four main phases: project initiation, system design, system building and evaluation, and user’s definitive acceptance. IDSSE-M is theoretically founded in Saxena’s Decision Support Engineering Methodology, and Turban and Aronson’s DSS Building Paradigm. Although IDSSE-M has been only used in academic settings, the complexity of the implementations has been high, and based on realistic organizational cases, with satisfactory results. Main benefit of IDSSE-M is providing a systematic software engineering oriented process for new developers of intelligent DMSS.

Methodological Considerations in Cognitive Science

This book is the result of a meeting where neuroscientists and computer scientits explored the possibility of developing common ground for the study of intelligent behavior in animals and machines. Here, we analyze some of the methodological obstacles that create conflicts among the different disciplines that form Cognitive Science. Our aim is to set forth specific methodological considerations that might lead towards the amelioration of the Isolation Problem which results from the fact that each area of Cognitive Science has been developed in almost complete independence of the others. We argue that Isolation Problem has been in great part generated by the misuse of linguistic terms associated with mental events.

Viewpoints and Controversies

At the Xalapa meeting, each session was followed by a lively discussion period of an hour or more which started with brief critiques of the papers by two panelists, and then opened up to a general discussion from the floor, with repeated involvement of the session’s speakers. Many viewpoints were offered, many controversies were aired, and we agreed that we should try to share some of the excitement with our readers. To this end, we selected about 30% of the material from the transcripts of the discussion sessions, and then edited it to provide a reasonably coherent narrative. As a result, the remarks of the discussants do not appear here verbatim. However, we have done our best to preserve the essence of each of the selected contributions, and offer our apologies for any cases (hopefully rare) where our attempts at brevity have altered the sense. [Editorial additions are set off in square parentheses.] Since the order of papers at the conference was different from the order of papers in the book, we have felt free to rearrange the discussion material under various headings which signal a number of the broad themes that emerged in the discussion. Our aim here is to let the reader share in the efforts made in Xalapa to see how very different contributions could be melded in our evolving appreciation of the commonalities and differences not only of Neuroscience and Artificial Intelligence but of the subdisciplines within these broad fields.

Improving IT Service Management with Decision-Making Support Systems

IT Service Management (ITSM) is a managerial approach to deliver value through IT services. This service-oriented world-view has required new knowledge on processes and tools to cope with the planning, design, building, operation, and improvement of IT services. While some decisions can be efficiently and effectively made by a manager alone, more complex decisions require further analysis and can benefit from the use of computerized Decision-Making Support Systems (DMSS). Although DMSS have been available for several decades, they have not been used to support ITSM processes and decisions. The purpose of this chapter is to demonstrate that ITSM would benefit from the utilization of DMSS. To this end, we provide a description of the decision-making process and decisional services embedded in the DMSS architecture and analyze how ITSM processes and decisions can be supported by DMSS. This chapter aims to foster more specific research on DMSS for ITSM.

Guest-editorial: Special issue title on engineering and management of IDTs for knowledge management systems

An ongoing and main challenge for intelligent decision technologies is the need to support knowledgeintensive tasks that usually are surging in multiple domains of applications such as manufacture [13], finance and insurance [10], and generic knowledge-based service business [12]. Engineering and management systems have relied on knowledge generated from several related areas including Decision Support Systems, Artificial Intelligence and Operations Research. Most recently, in the last decade, a business management perspective realized through Knowledge Management (KM) approach has been incorporated to this research stream driven by a knowledge-based services economy [9,11]. Thus, a new type of IT system called Knowledge Management System (KMS) [2] has emerged to leverage “professional and managerial activities by focusing on creating, gathering, organizing, and disseminating an organization’s “knowledge” as opposed to “information” or “data” (idem, p. 1). While KMS are engineered and managed by using multiple IT, we consider relevant the development of KMS based on intelligent decision technologies and the enhancement of the decision-making process [6]. Through following the seminal directions [7,8] established by the eminent AI scientists Herbert A. Simon (1916–2001) and Alan Newell (1927–1992), and the system’s emergent property established by the Theory of Systems [1,4], we support also the notion of a “distinct computer systems level, lying immediately above the symbol level, which is characterized by knowledge as the medium and the principle of rationality as the law of behavior” (Newell, p. 7) as a core conceptualization for the realization of such KMS. We believe that the five invited and peer-reviewed research papers in this special issue in “Engineering and Management of IDTs for Knowledge Management Systems”, advance our scientific knowledge on the state of the art of intelligent knowledge management systems in a context of decision-making process. One research paper reports an improved algorithm for an automatic joint of knowledge stored via ontologies. Three another research papers analyze deeply the KMS support challenges and the KMS emergent simulation-based design architectures and paradigms. Finally, a fifth paper, reviews the state of the art of KMS focused on the particular problem of improving the utilization of standards and models of process in the context of software and systems engineering. In first paper, titled “Automatic Fusion of knowledge stored in Ontologies”, Dr. Alma-Delia Cueva and Professor Adolfo Guzmán-Arenas (Computer Research Center, Instituto Politecnico Nacional, México), investigate the knowledge fusion problem which is a seamless process in human beings. However, for an automated system, authors report that algorithms of ontologies fusion lack of critical features such as the processing of synonyms, homonyms, redundancies, apparent contradictions, and inconsistencies. Authors, consequently presents a new method for ontology merging (OM), its algorithm and implementation to join two ontologies (obtained from Web documents) in an automatic fashion (without human intervention), producing a third ontology, and taking into account the problematic issues aforementioned, with a delivering result close to a human being performance. This paper contributes to the design of intelligent KMS with such a new method and algorithm. In the second paper, titled “Challenging Computer Software Frontiers and the Human Resistance to