Preference Structure Modelling for Multi-objective Decision Making: A Goal–Programming Approach

We propose a new approach for modelling preference structures in multi-objective decision-making (MODM) problems. The basic idea of the approach is to first develop PROMETHEE-influenced objective functions and then to use these to reformulate the problem as a distance-based goal–programming (GP) model. Three basic functional forms are proposed and explicit expressions are developed for them. Among other things, the expressions allow for the straightforward development of an interactive framework while keeping the information requirements from the decision maker (DM) at a minimum. An ‘automatic’ piecewise linear approximation scheme is proposed for solving the GP model. © 1997 by John Wiley & Sons, Ltd. J. Multi-Crit. Decis. Anal. 6 : 150–154 (1997) No. of Figures: 0. No. of Tables: 0. No. of References: 12.     

Utility Functions: A Compromise Programming Approach to Specification and Optimization

Nowadays, utility theory and compromise programming (CP) are considered very different paradigms and methodologies to measure preferences as well as to determine decision maker's optima on an efficient frontier. In this paper, however, we show that a utility function with separate variables (presented in the form of a Taylor series around the ideal point) is reducible to a weighted sum of CP distances. This linkage between utility and compromise (based on a main assumption in which the usual utility functions hold) leads to (i) a method for specification and optimization of usual utility functions by operational technique and (ii) a reformulation of standard CP with the advantage of determining the best CP solution from a utility perspective. © 1997 John Wiley & Sons, Ltd.     

A computational analysis of general intelligence tests for evaluating cognitive development

The progression in several cognitive tests for the same subjects at different ages provides valuable information about their cognitive development. One question that has caught recent interest is whether the same approach can be used to assess the cognitive development of artificial systems. In particular, can we assess whether the ‘fluid’ or ‘crystallised’ intelligence of an artificial cognitive system is changing during its cognitive development as a result of acquiring more concepts? In this paper, we address several IQ tests problems (odd-one-out problems, Raven’s Progressive Matrices and Thurstone’s letter series) with a general learning system that is not particularly designed on purpose to solve intelligence tests. The goal is to better understand the role of the basic cognitive operational constructs (such as identity, difference, order, counting, logic, etc.) that are needed to solve these intelligence test problems and serve as a proof-of-concept for evaluation in other developmental problems. From here, we gain some insights into the characteristics and usefulness of these tests and how careful we need to be when applying human test problems to assess the abilities and cognitive development of robots and other artificial cognitive systems.     

Epistemology and artificial intelligence

In this essay we advance the view that analytical epistemology and artificial intelligence are complementary disciplines. Both fields study epistemic relations, but whereas artificial intelligence approaches this subject from the perspective of understanding formal and computational properties of frameworks purporting to model some epistemic relation or other, traditional epistemology approaches the subject from the perspective of understanding the properties of epistemic relations in terms of their conceptual properties. We argue that these two practices should not be conducted in isolation. We illustrate this point by discussing how to represent a class of inference forms found in standard inferential statistics. This class of inference forms is interesting because its members share two properties that are common to epistemic relations, namely defeasibility and paraconsistency. Our modeling of standard inferential statistical arguments exploits results from both logical artificial intelligence and analytical epistemology. We remark how our approach to this modeling problem may be generalized to an interdisciplinary approach to the study of epistemic relations.     

An evolutionary system for neural logic networks using genetic programming and indirect encoding

Nowadays, intelligent connectionist systems such as artificial neural networks have been proved very powerful in a wide area of applications. Consequently, the ability to interpret their structure was always a desirable feature for experts. In this field, the neural logic networks (NLN) by their definition are able to represent complex human logic and provide knowledge discovery. However, under contemporary methodologies, the training of these networks may often result in non-comprehensible or poorly designed structures. In this work, we propose an evolutionary system that uses current advances in genetic programming that overcome these drawbacks and produces neural logic networks that can be arbitrarily connected and are easily interpretable into expert rules. To accomplish this task, we guide the genetic programming process using a context-free grammar and we encode indirectly the neural logic networks into the genetic programming individuals. We test the proposed system in two problems of medical diagnosis. Our results are examined both in terms of the solution interpretability that can lead in knowledge discovery, and in terms of the achieved accuracy. We draw conclusions about the effectiveness of the system and we propose further research directions.     

Logic programs and connectionist networks

One facet of the question of integration of Logic and Connectionist Systems, and how these can complement each other, concerns the points of contact, in terms of semantics, between neural networks and logic programs. In this paper, we show that certain semantic operators for propositional logic programs can be computed by feedforward connectionist networks, and that the same semantic operators for first-order normal logic programs can be approximated by feedforward connectionist networks. Turning the networks into recurrent ones allows one also to approximate the models associated with the semantic operators. Our methods depend on a well-known theorem of Funahashi, and necessitate the study of when Funahashi's theorem can be applied, and also the study of what means of approximation are appropriate and significant.     

Methodological naturalism and epistemic internalism

Epistemic naturalism holds that the results or methodologies from the cognitive sciences are relevant to epistemology, and some have maintained that scientific methods are more compatible with externalist theories of justification than with internalist theories. But practically all discussions about naturalized epistemology are framed exclusively in terms of cognitive psychology, which is only one of the cognitive sciences. The question addressed in this essay is whether a commitment to naturalism really does favor externalism over internalism, and we offer reasons for thinking that naturalism in epistemology is compatible with both internalist and externalist conceptions of justification. We also argue that there are some distinctively internalist aims that are currently being studied scientifically and these notions, and others, should be studied by scientific methods. This essay is dedicated to Deborah Mayo, who has long advocated using error statistical techniques to analyze and resolve epistemological puzzles in the philosophy of science. This essay follows the same spirit by advocating that computational concepts and techniques be applied within the heart of traditional, analytic epistemology.     

Motor programming in apraxia of speech

Apraxia of Speech (AOS) is an impairment of motor programming. However, the exact nature of this deficit remains unclear. The present study examined motor programming in AOS in the context of a recent two-stage model [Klapp, S. T. (1995). Motor response programming during simple and choice reaction time: The role of practice. Journal of Experimental Psychology: Human Perception and Performance, 21, 1015–1027; Klapp, S. T. (2003). Reaction time analysis of two types of motor preparation for speech articulation: Action as a sequence of chunks. Journal of Motor Behavior, 35, 135–150] that proposes a preprogramming stage (INT) and a process that assigns serial order to multiple programs in a sequence (SEQ). The main hypothesis was that AOS involves a process-specific deficit in the INT (preprogramming) stage of processing, rather than in the on-line serial ordering (SEQ) and initiation of movement. In addition, we tested the hypothesis that AOS involves a central (i.e., modality-general) motor programming deficit. We used a reaction time paradigm that provides two dependent measures: study time (the amount of time for participants to ready a motor response; INT), and reaction time (time to initiate movement; SEQ). Two experiments were conducted to examine INT and SEQ in AOS: Experiment 1 involved finger movements, Experiment 2 involved speech movements analogous to the finger movements. Results showed longer preprogramming time for patients with AOS but normal sequencing and initiation times, relative to controls. Together, the findings are consistent with the hypothesis of a process-specific, but central (modality-independent) deficit in AOS; alternative explanations are also discussed.     

Change detection evokes a Simon-like effect

The present experiment studied choice response context effects on the programming of response sequences using behavioural and electrophysiological methods. Participants were asked to produce responses differing in sequence length (1-key vs. 3-key responses) with either their left or right hand in a choice reaction time (RT) task. The choice response context was manipulated by a blocked or mixed execution of 1-key and 3-key responses. A sequence length effect on RT was observed in the blocked but not in the mixed condition. The time course of the lateralized readiness potential indicates a motoric locus of the sequence length effect, suggesting that the response hand is activated before the entire motor program is established.     

Optimum examinee samples for item parameter estimation in item response theory: A multi-objective programming approach

This paper proposes a multi-objective programming method for determining samples of examinees needed for estimating the parameters of a group of items. In the numerical experiments, optimum samples are compared to uniformly and normally distributed samples. The results show that the samples usually recommended in the literature are well suited for estimating the difficulty parameters. Furthermore, they are also adequate for estimating the discrimination parameters in the three-parameter model, butnot for the guessing parameters.