Uncertainty reasoning and representation: A Comparison of several alternative approaches

Much of the research done in Artificial Intelligence involves investigating and developing methods of incorporating uncertainty reasoning and representation into expert systems. Several methods have been proposed and attempted for handling uncertainty in problem solving situations. The theories range from numerical approaches based on strict probabilistic reasoning to non-numeric approaches based on logical reasoning. This study investigates a number of these approaches including Bayesian Probability, Mycin Certainty Factors, Dempster-Shafer Theory of Evidence, Fuzzy Set Theory, Possibility Theory and non monotonic logic. Each of these theories and their underlying formalisms are explored by means of examples. The discussion concentrates on a comparison of the different approaches, noting the type of uncertainty that they best represent

Fuzzy cognitive mapping to support multi-agent decisions in development of urban policymaking

The awareness about environmental complexity involves real-time knowledge and demands urban planning initiatives. Knowledge is multiform, multi-agent and mirrors environmental complexity. Problems characterizing urban sustainability particularly claim non-expert knowledge, being informal, puzzling, uncertain, incomplete, hard to be handled, formalized, modelled. This study utilizes Fuzzy cognitive maps to explore such complexity and support multiagent decisions. It concerns the scenario-building process of the new plan of Taranto (Italy), a paradigmatic example of decaying industrial area, heavily characterized by social fragmentation and environment degradation. This approach aims at structuring environmental problems, modelling future strategies and contributing to build a multi-agent decision support system for complex urban planning contexts

Fuzzy argumentation for trust

In an open Multi-Agent System, the goals of agents acting on behalf of their owners often conflict with each other. Therefore, a personal agent protecting the interest of a single user cannot always rely on them. Consequently, such a personal agent needs to be able to reason about trusting (information or services provided by) other agents. Existing algorithms that perform such reasoning mainly focus on the immediate utility of a trusting decision, but do not provide an explanation of their actions to the user. This may hinder the acceptance of agent-based technologies in sensitive applications where users need to rely on their personal agents. Against this background, we propose a new approach to trust based on argumentation that aims to expose the rationale behind such trusting decisions. Our solution features a separation of opponent modeling and decision making. It uses possibilistic logic to model behavior of opponents, and we propose an extension of the argumentation framework by Amgoud and Prade to use the fuzzy rules within these models for well-supported decisions

A Theory of Factfinding: The Logic for Processing Evidence

Academics have never agreed on a theory of proof. The darkest corner of anyone’s theory has concerned how legal decisionmakers logically should find facts. This Article pries open that cognitive black box. It does so by employing multivalent logic, which enables it to overcome the traditional probability problems that impeded all prior attempts. The result is the first-ever exposure of the proper logic for finding a fact or a case’s facts. The focus will be on the evidential processing phase, rather than the application of the standard of proof as tracked in my prior work. Processing evidence involves (1) reasoning inferentially from a piece of evidence to a degree of belief and of disbelief in the element to be proved, (2) aggregating pieces of evidence that all bear to some degree on one element in order to form a composite degree of belief and of disbelief in the element, and (3) considering the series of elemental beliefs and disbeliefs to reach a decision. Zeroing in, the factfinder in step #1 should connect each item of evidence to an element to be proved by constructing a chain of inferences, employing multivalent logic’s usual rules for conjunction and disjunction to form a belief function that reflects the belief and the disbelief in the element and also the uncommitted belief reflecting uncertainty. The factfinder in step #2 should aggregate, by weighted arithmetic averaging, the belief functions resulting from all the items of evidence that bear on any one element, creating a composite belief function for the element. The factfinder in step #3 does not need to combine elements, but instead should directly move to testing whether the degree of belief from each element’s composite belief function sufficiently exceeds the corresponding degree of disbelief. In sum, the factfinder should construct a chain of inferences to produce a belief function for each item of evidence bearing on an element, and then average them to produce for each element a composite belief function ready for the element-by-element standard of proof. This Article performs the task of mapping normatively how to reason from legal evidence to a decision on facts. More significantly, it constitutes a further demonstration of how embedded the multivalent-belief model is in our law

Staying Faithful to the Standards of Proof

Academics have never quite understood the standards of proof or, indeed, much about the theory of proof Their formulations beget probabilistic musings, which beget all sorts of paradoxes, which in turn beget radical reconceptions and proposals for reform. The theoretical radicals argue that the law needs some basic reconception such as recognizing the aim of legal proof as not at all a search for truth but rather the production of an acceptable result, or that the law needs some shattering reform such as greatly heightening the civil standard of proof on each part of the case to ensure a more-likely than- not overall result. This Article refutes all those baroque rereadings. It shows that the standards of proof properly understood on the law\u27s own terms without a probabilistic overlay, work just fine. The law tells factfinders to compare their degree of belief in the alleged fact to their degree of contradictory disbelief. Obeying that instruction resolves mathematically the paradoxes that traditional probability theory creates for itself Most surprising, the burden of proof by which the proponent must prove all the elements and the opponent need disprove only one, does not produce an asymmetry between the parties. The law\u27s standards of proof need no drastic reconception or reform, because the law knew what it was doing all along. It deals with factual beliefs in a world that will remain uncertain, not with the odds of the facts becoming certain. And the well-established mathematics of beliefs are not the mathematics of odds

AHP and uncertainty theories for decision making using the ER-MCDA methodology

International audienceIn this paper, we present the ER-MCDA methodology for multi-criteria decision-making based on imperfect information coming from more or less reliable and conflicting sources. The Analytic Hierarchy Process (AHP), Fuzzy Sets, Possibility and Belief Functions theories are combined to take a decision based on imprecise and uncertain evaluations of quantitative, qualitative criteria. Classical aggregation of criteria is replaced by a two-step fusion process using advanced fusion rules based on the Dezert-Smarandache Theory (DSmT) that allows to make a difference between importance, reliability and uncertainty of information sources and contents

On the Mental State of Consciousness of Wrongdoing

Mistake about or ignorance of the law does not exculpate in criminal law, except in limited circumstances. Doctrine and theory cognate to this principle are, by now, well developed and understood. But might an actor\u27s awareness of the illegality or wrongfulness of her conduct inculpate — that is, constitute a form of mens rea that establishes or aggravates liability? Trends in recent adjudication in white collar crime suggest that the answer is yes. This article, part of a symposium issue on Adjudicating the Guilty Mind, takes the first pass at describing the mental state of “consciousness of wrongdoing,” assessing its fit with the conceptual architecture of substantive criminal law, and uncovering the many challenges of proof and adjudication that this concept poses. Three conclusions broadly emerge from this initial, and somewhat truncated, inquiry: first, inculpating an actor for adverting to the legal or normative significance of her conduct is an attractive means of dealing with difficult line-drawing problems presented by many white collar offenses; second, the method can be justified on both retributive and deterrent grounds; and third, the practice requires much more thought and precision at the operational level, lest problems inherent in the structure of criminal adjudication be exacerbated in cases in which liability depends on the idea that an actor “knew what she was doing was wrong