Generalized State Values in an Anticipatory Learning Classifier System
This paper introduces generalized state values to the anticipatory learning classifier system ACS2. Previous studies showed that the evolving generalized state value in ACS2 might be overgeneral for a proper policy representation. Thus, the policy representation is separated from the model representation. A function approximation module is added that approximates state values. Actual action choice then depends on the learned generalized state values predicted by the means of the predictive model yielding anticipatory behavior. It is shown that the function approximation module accurately generalizes the state value function in the investigated MDP. Improvement of the approach by the means of further anticipatory interaction between predictive model learner and state value learner is suggested. We also propose the implementation of task dependent anticipatory attentional mechanisms exploiting the representation of the generalized state-value function. Finally, the anticipatory framework may be extended to support multiple motivations integrated in a motivational module which could be influenced by emotional biases.

title: SEARCH, Blackbox Optimization, And Sample Complexity
abstract: The SEARCH (Search Envisioned As Relation and Class Hierarchizing) framework developed elsewhere (Kargupta, 1995; Kargupta and Goldberg, 1995) offered an alternate perspective toward blackbox optimization -- optimization in presence of little domain knowledge. The SEARCH framework investigates the conditions essential for transcending the limits of random enumerative search using a framework developed in terms of relations, classes and partial ordering. This paper presents a summary of some of the main results of that work. A closed form bound on the sample complexity in terms of the cardinality of the relation space, class space, desired quality of the solution and the reliability is presented. This also leads to the identification of the class of order-k delineable problems that can be solved in polynomial sample complexity. These results are applicable to any blackbox search algorithms, including evolutionary optimization techniques.

title: The Nonuniform Walsh-Schema Transform
abstract: The Walsh-schema transform allows useful analysis of the expected performance of genetic algorithms. Until recently, its use has been restricted to the static analysis of functions, codings, and operators. In this paper, we present a form of the Walsh-schema transform that accounts for nonuniformly distributed populations, and thereby extend its use to the dynamic analysis of genetic algorithms. We also show the relationship between the nonuniform Walsh-schema transform and the hyperplane transform.

title: Construction of high-order deceptive functions using low-order Walsh coefficients
abstract: This paper constructs deceptive functions over bit strings of any length using low-order Walsh coefficients. Specifically, a partially deceptive construct is achieved with coefficients no higher than second order, and a fully deceptive construct is achieved with coefficients no higher than third order. Previous work on deception tacitly assumed that the very highest order Walsh coefficient, the order-l coefficient in a length-l problem, must be non-zero to achieve full deception, that condition where all schemata of orderl−1 or less containing the complement of the global optimum are superior to their competitors. The constructions of this paper show such assumptions to be false and lead to a more general theory of functions whose value only depends on the number of ones (or zeros) in the function's argument, so-calledfunctions of unitation. These results also help shed some light on Tanese's puzzling results on functions that contained large numbers of Walsh terms of fixed order.

title: Population sizing for entropy-based model building in discrete estimation of distribution algorithms
abstract: This paper proposes a population-sizing model for entropy-based model building in discrete estimation of distribution algorithms. Specifically, the population size required for building an accurate model is investigated. The effect of selection pressure on population sizing is also preliminarily incorporated. The proposed model indicates that the population size required for building an accurate model scales as Θ(m log m), where m is the number of substructures of the given problem and is proportional to the problem size. Experiments are conducted to verify the derivations, and the results agree with the proposed model.

title: Classifier Systems and Genetic Algorithms
abstract: Classifier systems are massively parallel, message-passing, rule-based systems that learn through credit assignment (the bucket brigade algorithm) and rule discovery (the genetic algorithm). They typically operate in environments that exhibit one or more of the following characteristics: (1) perpetually novel events accompanied by large amounts of noisy or irrelevant data; (2) continual, often real-time, requirements for action; (3) implicitly or inexactly defined goals; and (4) sparse payoff or reinforcement obtainable only through long action sequences. Classifier systems are designed to absorb new information continuously from such environments, devising sets of competing hypotheses (expressed as rules) without disturbing significantly capabilities already acquired. This paper reviews the definition, theory, and extant applications of classifier systems, comparing them with other machine learning techniques, and closing with a discussion of advantages, problems, and possible extensions of classifier systems.

title: Teching PL/I using a microcomputer
abstract: Until the Fall 1983 semester, the introductory programming course at Brooklyn College had been taught (in the PL/C dialect of PL/I) using keypunch equipment in a batch environment. However, that term two experimental sections used IBM Personal Computer (PC's) instead of keypunches. The experiment was expanded in the Spring 1984 semester to include almost one third of all sections of the introductory course, and then expanded again in the Fall 1984 semester to all sections of the first PL/I course, plus three sections of second-level courses. Although the PC's were quite easy to use, the PL/I-86 PC-DOS compiler produced by Digital Research caused many problems. A number of advantages and disadvantages of the experiment are discussed. Other implications of the use of PC's are also noted, including the possible change of language and the extension to more advanced courses in the curriculum.