This paper describes comparisons of the minimax backup rule and the product back-up rule on a wide variety of games, including P-games, G-games, three-hole kalah, Othello, and Ballard’s incremental game. In three-hole kalah, the product rule plays better than a minimax search to the same depth. This is a remarkable result, since it is the first widely known game in which product has been found ...

This paper presents an empirical investigation of the recently proposed k-Nearest Centroid Neighbours (k-NCN) classi®cation rule along with two heuristic modi®cations of it. These alternatives make use of both proximity and geometrical distribution of the prototypes in the training set in order to estimate the class label of a given sample. The experimental results show that both alternatives g...

The goal of this paper is to investigate to what extent a rule learning heuristic can be learned from experience. Our basic approach is to learn a large number of rules and record their performance on the test set. Subsequently, we train regression algorithms on predicting the test set performance from training set characteristics. We investigate several variations of this basic scenario, inclu...

Association rule mining is one of the widely using and simple concepts to find the frequent item sets from large number of datasets. While generating frequent item sets from a large dataset using association rule mining is not so efficient. This can be improved by using particle swarm optimization algorithm (PSO). PSO algorithm is population based evolutionary heuristic search methods used for ...

A common action selection mechanism used in agent-oriented programming is to base action selection on a set of rules. Since rules need not be mutually exclusive, agents are often underspecified. This means that the decision-making of such agents leaves room for multiple choices of actions. Underspecification implies there is potential for improvement or optimalization of the agent’s behavior. S...

A Distributed Deductive Database System consists of many autonomous deductive database systems connected by a computer network to facilitate sharing of both database and rules. The design of a distributed deductive database system diiers from the design of conventional non-distributed deductive database systems. The former requires design of distribution of both the database and rulebase, where...

In this paper, we propose a novel approach for learning regression rules by transforming the regression problem into a classification problem. Unlike previous approaches to regression by classification, in our approach the discretization of the class variable is tightly integrated into the rule learning algorithm. The key idea is to dynamically define a region around the target value predicted ...

In this paper we discuss a heuristically control led approach to combining reasoning w i th cases and reasoning w i th rules. Our task is interpretat ion of under-defined terms that occur in legal statutes (like the Internal Revenue Code) where certain terms must be applied to part icular cases even though their meanings are not defined by the statute and the statutory rules are unclear as to s...

In this paper, we propose a simple heuristic approach for the inventory control problem with stochastic demand and multiplicative random yield. Our heuristic tries to find the best candidate within a class of policies which are referred to in the literature as the linear inflation rule (LIR) policies. Our approach is computationally fast, easy to implement and intuitive to understand. Moreover,...

This paper presents a genetic algorithm for the Resource Constrained Project Scheduling Problem (RCPSP). The chromosome representation of the problem is based on random keys. The schedule is constructed using a heuristic priority rule in which the priorities of the activities are defined by the genetic algorithm. The heuristic generates parameterized active schedules. The approach was tested on...