An argumentative formalism for implementing rational agents

The design of intelligent agents is a key issue for many applications. Although there is no universally accepted de nition of intelligence, a notion of rational agency has been proposed as an alternative for the characterization of intelligent agency. Modeling the epistemic state of a rational agent is one of the most di cult tasks to be addressed in the design process, and its complexity is directly related to the formalism used for representing the knowledge of the agent. This paper presents the main features of Observation-based Defeasible Logic Programming (ODeLP), a formalism tailored for agents that perform defeasible reasoning in dynamic domains. Most agents must have a timely interaction with their environment. Since the cognitive process of rational agents is complex and computationally expensive, this interaction is particularly hard to achieve. To solve this issue, we propose an optimization of the inference process in ODeLP based on the use of precompiled knowledge. This optimization can be e ciently implemented using concepts from pattern matching algorithms.Eje: Sistemas inteligentesRed de Universidades con Carreras en Informática (RedUNCI

An argumentative formalism for implementing rational agents

The design of intelligent agents is a key issue for many applications. Although there is no universally accepted de nition of intelligence, a notion of rational agency has been proposed as an alternative for the characterization of intelligent agency. Modeling the epistemic state of a rational agent is one of the most di cult tasks to be addressed in the design process, and its complexity is directly related to the formalism used for representing the knowledge of the agent. This paper presents the main features of Observation-based Defeasible Logic Programming (ODeLP), a formalism tailored for agents that perform defeasible reasoning in dynamic domains. Most agents must have a timely interaction with their environment. Since the cognitive process of rational agents is complex and computationally expensive, this interaction is particularly hard to achieve. To solve this issue, we propose an optimization of the inference process in ODeLP based on the use of precompiled knowledge. This optimization can be e ciently implemented using concepts from pattern matching algorithms.Eje: Sistemas inteligentesRed de Universidades con Carreras en Informática (RedUNCI

A Knowledge-Based Expert Systems Primer and Catalog

For more than 20 years, artificial intelligence techniques have been applied to the development of computer programs that solve difficult problems. Although several expert systems are well known, it is all too easy to circumscribe the field based on these few examples. The purpose of this paper is to present the fundamentals of the field (the Primer), and to give a broad overview via concise descriptions of many rule-based expert systems and knowledge engineering frameworks (the Catalog)

A neural network and rule based system application in water demand forecasting

This thesis was submitted for the degree of Doctor of Philosophy and was awarded by Brunel University.This thesis describes a short term water demand forecasting application that is based upon a combination of a neural network forecast generator and a rule based system that modifies the resulting forecasts. Conventionally, short term forecasting of both water consumption and electrical load demand has been based upon mathematical models that aim to either extract the mathematical properties displayed by a time series of historical data, or represent the causal relationships between the level of demand and the key factors that determine that demand. These conventional approaches have been able to achieve acceptable levels of prediction accuracy for those days where distorting, non cyclic influences are not present to a significant degree. However, when such distortions are present, then the resultant decrease in prediction accuracy has a detrimental effect upon the controlling systems that are attempting to optimise the operation of the water or electricity supply network. The abnormal, non cyclic factors can be divided into those which are related to changes in the supply network itself, those that are related to particular dates or times of the year and those which are related to the prevailing meteorological conditions. If a prediction system is to provide consistently accurate forecasts then it has to be able to incorporate the effects of each of the factor types outlined above. The prediction system proposed in this thesis achieves this by the use of a neural network that by the application of appropriately classified example sets, can track the varying relationship between the level of demand and key meteorological variables. The influence of supply network changes and calendar related events are accounted for by the use of a rule base of prediction adjusting rules that are built up with reference to past occurrences of similar events. The resulting system is capable of eliminating a significant proportion of the large prediction errors that can lead to non optimal supply network operation

Artificial Intelligence and Human Error Prevention: A Computer Aided Decision Making Approach: Technical Report No. 4: Survey and Analysis of Research on Learning Systems from Artificial Intelligence

Coordinated Science Laboratory was formerly known as Control Systems LaboratoryU.S. Department of Transportation / DOT FA79WA-4360 ABFederal Aviation Administratio