Towards efficient default reasoning

A decision method for Reiter's default logic is developed. It can determine whether a default theory has an extension, whether a formula is in some extension of a default theory and whether a formula is in every extension of a default theory. The method handles full propositional default logic. It can be implemented to work in polynomial space and by using only a theorem prover for the underlying propositional logic as a subroutine. The method divides default reasoning into two major subtasks: the search task of examining every alternative for extensions, which is solved by backtracking search, and the classical reasoning task, which can be implemented by a theorem prover for the underlying classical logic. Special emphasis is given to the search problem. The decision method employs a new compact representation of extensions which reduces the search space. Efficient techniques for pruning the search space further are developed

Limit Deciding Dispositions. A Metaphysical Symmetry-Breaker for the Limit Decision Problem

There are basically four options to which state the limiting instant in a change from one state to its opposite belongs – only the first, only the second, both or none. This situation is usually referred to as the limit decision problem since all of these options seem troublesome: The first two alleged solutions are asymmetric and thus need something to ground this asymmetry in (a symmetry-breaker); while the last two options leave the realm of classical logic. I argue that including the debate about dispositions enables new options for solutions to the temporal limit decision problem. Metaphysical considerations function as a symmetry-breaker and thus remove the need for a non-classical solution. Dispositions bring about the changes in the world, so they constitute the metaphysical background for the instant of change. In particular, I argue that according to the triadic process account of dispositions, the limiting instant belongs to the second interval and only the second interval.There are basically four options to which state the limiting instant in a change from one state to its opposite belongs – only the first, only the second, both or none. This situation is usually referred to as the limit decision problem since all of these options seem troublesome: The first two alleged solutions are asymmetric and thus need something to ground this asymmetry in (a symmetry-breaker); while the last two options leave the realm of classical logic. I argue that including the debate about dispositions enables new options for solutions to the temporal limit decision problem. Metaphysical considerations function as a symmetry-breaker and thus remove the need for a non-classical solution. Dispositions bring about the changes in the world, so they constitute the metaphysical background for the instant of change. In particular, I argue that according to the triadic process account of dispositions, the limiting instant belongs to the second interval and only the second interval

Perspectives on Correctness in Probabilistic Inference from Psychology

Research into decision making has enabled us to appreciate that the notion of correctness is multifaceted. Different normative framework for correctness can lead to different insights about correct behavior. We illustrate the shifts for correctness insights with two tasks, the Wason selection task and the conjunction fallacy task; these tasks have had key roles in the development of logical reasoning and decision making research respectively. The Wason selection task arguably has played an important part in the transition from understanding correctness using classical logic to classical probability theory (and information theory). The conjunction fallacy has enabled a similar shift from baseline classical probability theory to quantum probability. The focus of this overview is the latter, as it represents a novel way for understanding probabilistic inference in psychology. We conclude with some of the current challenges concerning the application of quantum probability theory in psychology in general and specifically for the problem of understanding correctness in decision making

Fuzzy-AHP Application to Country Risk Assessment

In solving various real-world decision situations, it is necessary to handle uncertainties efficiently and effectively. In the past, rule-based expert systems have been used for handling uncertainty in such problems. Generally, the expert systems are based on classical logic and developers need to add special methods for handling uncertainty. Some of the methods used for handling uncertainty in expert systems include heuristic approaches, probability theory, possibility theory, and fuzzy theory. Fuzzy reasoning and logic offers a more natural way of handling uncertainty. All propositions can be modeled by possibility distributions over appropriate domains.Fuzzy reasoning processis similar tohuman logical reasoning. This paper presents a fuzzy version ofanalytic hierarchy processto country risk assessment problem

Descriptive Complexity, Computational Tractability, and the Logical and Cognitive Foundations of Mathematics

In computational complexity theory, decision problems are divided into complexity classes based on the amount of computational resources it takes for algorithms to solve them. In theoretical computer science, it is commonly accepted that only functions for solving problems in the complexity class P, solvable by a deterministic Turing machine in polynomial time, are considered to be tractable. In cognitive science and philosophy, this tractability result has been used to argue that only functions in P can feasibly work as computational models of human cognitive capacities. One interesting area of computational complexity theory is descriptive complexity, which connects the expressive strength of systems of logic with the computational complexity classes. In descriptive complexity theory, it is established that only first-order (classical) systems are connected to P, or one of its subclasses. Consequently, second-order systems of logic are considered to be computationally intractable, and may therefore seem to be unfit to model human cognitive capacities. This would be problematic when we think of the role of logic as the foundations of mathematics. In order to express many important mathematical concepts and systematically prove theorems involving them, we need to have a system of logic stronger than classical first-order logic. But if such a system is considered to be intractable, it means that the logical foundation of mathematics can be prohibitively complex for human cognition. In this paper I will argue, however, that this problem is the result of an unjustified direct use of computational complexity classes in cognitive modelling. Placing my account in the recent literature on the topic, I argue that the problem can be solved by considering computational complexity for humanly relevant problem solving algorithms and input sizes.Peer reviewe