21,081 research outputs found
Arguments Whose Strength Depends on Continuous Variation
Both the traditional Aristotelian and modern symbolic approaches to logic have seen logic in terms of discrete symbol processing. Yet there are several kinds of argument whose validity depends on some topological notion of continuous variation, which is not well captured by discrete symbols. Examples include extrapolation and slippery slope arguments, sorites, fuzzy logic, and those involving closeness of possible worlds. It is argued that the natural first attempts to analyze these notions and explain their relation to reasoning fail, so that ignorance of their nature is profound
The Archimedean trap: Why traditional reinforcement learning will probably not yield AGI
After generalizing the Archimedean property of real numbers in such a way as to make it adaptable to non-numeric structures, we demonstrate that the real numbers cannot be used to accurately measure non-Archimedean structures. We argue that, since an agent with Artificial General Intelligence (AGI) should have no problem engaging in tasks that inherently involve non-Archimedean rewards, and since traditional reinforcement learning rewards are real numbers, therefore traditional reinforcement learning probably will not lead to AGI. We indicate two possible ways traditional reinforcement learning could be altered to remove this roadblock
New perspectives on realism, tractability, and complexity in economics
Fuzzy logic and genetic algorithms are used to rework more realistic (and more complex) models of competitive markets. The resulting equilibria are significantly different from the ones predicted from the usual static analysis; the methodology solves the Walrasian problem of how markets can reach equilibrium, starting with firms trading at disparate prices.
The modified equilibria found in these complex market models involve some mutual self-restraint on the part of the agents involved, relative to economically rational behaviour. Research (using similar techniques) into the evolution of collaborative behaviours in economics, and of altruism generally, is summarized; and the joint significance of these two bodies of work for public policy is reviewed.
The possible extension of the fuzzy/ genetic methodology to other technical aspects of economics (including international trade theory, and development) is also discussed, as are the limitations to the usefulness of any type of theory in political domains. For the latter purpose, a more differentiated concept of rationality, appropriate to ill-structured choices, is developed. The philosophical case for laissez-faire policies is considered briefly; and the prospects for change in the way we ‘do economics’ are analysed
Concepts and Their Dynamics: A Quantum-Theoretic Modeling of Human Thought
We analyze different aspects of our quantum modeling approach of human
concepts, and more specifically focus on the quantum effects of contextuality,
interference, entanglement and emergence, illustrating how each of them makes
its appearance in specific situations of the dynamics of human concepts and
their combinations. We point out the relation of our approach, which is based
on an ontology of a concept as an entity in a state changing under influence of
a context, with the main traditional concept theories, i.e. prototype theory,
exemplar theory and theory theory. We ponder about the question why quantum
theory performs so well in its modeling of human concepts, and shed light on
this question by analyzing the role of complex amplitudes, showing how they
allow to describe interference in the statistics of measurement outcomes, while
in the traditional theories statistics of outcomes originates in classical
probability weights, without the possibility of interference. The relevance of
complex numbers, the appearance of entanglement, and the role of Fock space in
explaining contextual emergence, all as unique features of the quantum
modeling, are explicitly revealed in this paper by analyzing human concepts and
their dynamics.Comment: 31 pages, 5 figure
Probabilistic Dynamic Logic of Phenomena and Cognition
The purpose of this paper is to develop further the main concepts of
Phenomena Dynamic Logic (P-DL) and Cognitive Dynamic Logic (C-DL), presented in
the previous paper. The specific character of these logics is in matching
vagueness or fuzziness of similarity measures to the uncertainty of models.
These logics are based on the following fundamental notions: generality
relation, uncertainty relation, simplicity relation, similarity maximization
problem with empirical content and enhancement (learning) operator. We develop
these notions in terms of logic and probability and developed a Probabilistic
Dynamic Logic of Phenomena and Cognition (P-DL-PC) that relates to the scope of
probabilistic models of brain. In our research the effectiveness of suggested
formalization is demonstrated by approximation of the expert model of breast
cancer diagnostic decisions. The P-DL-PC logic was previously successfully
applied to solving many practical tasks and also for modelling of some
cognitive processes.Comment: 6 pages, WCCI 2010 IEEE World Congress on Computational Intelligence
July, 18-23, 2010 - CCIB, Barcelona, Spain, IJCNN, IEEE Catalog Number:
CFP1OUS-DVD, ISBN: 978-1-4244-6917-8, pp. 3361-336
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