The economic theory of rationality promises to equal mathematical logic in its importance for the mechanization of reasoning. We survey the growing literature on how the basic notions of probability, utility, and rational choice, coupled with practical limitations on information and resources, influence the design and analysis of reasoning and representation systems. 1 Introduction People make judgments of rationality all the time, usually in criticizing someone else's thoughts or deeds as irrational, or in defending their own as rational. Artificial intelligence researchers construct systems and theories to perform or describe rational thought and action, criticizing and defending these systems and theories in terms similar to but more formal than those of the man or woman on the street. Judgments of human rationality commonly involve several different conceptions of rationality, including a logical conception used to judge thoughts, and an economic one used to judge actions or...

A vast amount of work has been done in recent years on the design, analysis, implementation and verification of special purpose parallel computing systems. This paper presents a survey of various aspects of this work. A long, but by no means complete, bibliography is given. 1. Introduction Turing [365] demonstrated that, in principle, a single general purpose sequential machine could be designed which would be capable of efficiently performing any computation which could be performed by a special purpose sequential machine. The importance of this universality result for subsequent practical developments in computing cannot be overstated. It showed that, for a given computational problem, the additional efficiency advantages which could be gained by designing a special purpose sequential machine for that problem would not be great. Around 1944, von Neumann produced a proposal [66, 389] for a general purpose storedprogram sequential computer which captured the fundamental principles of...

The design-based approach is a methodology for investigating mechanisms capable of generating mental phenomena, whether introspectively or externally observed, and whether they occur in humans, other animals or robots. The study of designs satisfying requirements for autonomous agency can provide new deep theoretical insights at the information processing level of description of mental mechanisms. Designs for working systems (whether on paper or implemented on computers) can systematically explicate old explanatory concepts and generate new concepts that allow new and richer interpretations of human phenomena. To illustrate this, some aspects of human grief are analysed in terms of a particular information processing architecture being explored in our research group. We do not claim that this architecture is part of the causal structure of the human mind; rather, it represents an early stage in the iterative search for a deeper and more general architecture, capable of explaining more...

this document. 9.5.2 A comparison of CUE and libido

Physical systems are by nature continuous, but often display nonlinear behaviors that makes them hard to analyze. Typically, these nonlinearities occur at a time scale that is much smaller than the time scale at which gross system behavior needs to be described. In other situations, nonlinear e ects are small and of a parasitic nature. To achieve e ciency and clarity in building complex system models, and to reduce computational complexity in the analysis of system behavior, modelers often abstract away anypara sitic component parameter e ects, and analyze the system at more abstract time scales. However, these abstractions often introduce abrupt, instantaneous changes in system behavior. To accommodate mixed continuous and discrete behavior, this paper develops a hybrid modeling formalism that dynamically constructs bond graph model fragments that govern system behavior during continuous operation. When threshold values are crossed, a meta-level control model invokes discontinuous state and model con guration changes. Discontinuities violate physical principles of conservation of energy and continuity of power, but the principle of invariance ofstategoverns model behavior when the control module is active. Conservation of energy and continuity ofpower again govern behavior generation as soon as a new model con guration is established. This allows for maximally constrained continuous model fragments. The two primary contributions of this paper are an algorithm for inferring the correct new mode and state variable values in the hybrid modeling framework, and a veri cation scheme that ensures hybrid models conform to physical system principles based on the principles of divergence of time and temporal evolution in behavior transtions. These principles are employed in energy phase space analysis to verify physical consistency of models.

This article reviews the limitations of the standard computing paradigm and sketches the concept of quantum neural computing. Implications of this idea for the understanding of biological information processing and design of new kinds of computing machines are described. Arguments are presented in support of the thesis that brains are to be viewed as quantum systems with their neural structures representing the classical measurement hardware. From a performance point of view, a quantum neural computer may be viewed as a collection of many conventional computers that are designed to solve different problems. A quantum neural computer is a single machine that reorganizes itself, in response to a stimulus, to perform a useful computation. Selectivity offered by such a reorganization appears to be at the basis of the gestalt style of biological information processing. Clearly, a quantum neural computer is more versatile than the conventional computing machine.

The dichotomy between endophysical/intrinsic and exophysical/extrinsic perception concerns the question of how a model - mathematical, logical, computational - universe is perceived from inside or from outside, [71, 65, 66, 59, 60, 68, 67]. This distinction goes back in time at least to Archimedes, reported to have asked for a point outside the world from which one could move the earth. An exophysical perception is realized when the system is laid out and the experimenter peeps at the relevant features without changing the system. The information flows on a one-way road: from the system to the experimenter. An endophysical perception can be realized when the experimenter is part of the system under observation. In such a case one has a two-way informational flow; measurements and entities measured are interchangeable and any attempt to distinguish between them ends up as a convention. The general conception dominating the sciences is that the physical universe is perceivable ...

Gödel's Incompleteness Theorem asserts that any sufficiently rich, sound, and recursively axiomatizable theory is incomplete. We show that, in a quite general topological sense, incompleteness is a rather common phenomenon: With respect to any reasonable topology the set of true and unprovable statements of such a theory is dense and in many cases even co-rare.

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This paper starts out from two observations: firstly, that there are complex links between what we term intelligence and what we term creativity and, secondly, that the phenomenon of surprise has a significant role in both the genesis and evaluation of creativity, and is tightly coupled to perception.