On the Nature of Intelligence: The Relevance of Statistical Mechanics

A conundrum that results from the normal distribution of intelligence is explored. The conundrum concerns the chief characteristic of intelligence, the ability to find order in the world (or to know the world) on the one hand, and the random processes that are the foundation of the normal distribution on the other. Statistical mechanics is explored to help in understanding the relation between order and randomness in intelligence. In statistical mechanics, ordered phenomena, like temperature or chemical potential, can be derived from random processes, and empirical data indicate that such a relationship between ordered phenomena and random processes must exist as regards intellect. The apparent incongruity in having both order and randomness characterize intelligence is found to be a feature that allows for intelligence to be known without recourse to underpinnings that are independent of the knowing individual. The contrast of ordered processes and random processes indicates that probabilistic knowledge of the world, stemming from the latter processes, is a basis for knowing the world in a fundamental manner, whether the concern is the physical world or mind. It is likely that physiological concomitants involved in the development, and perhaps current operation, of intellect also demonstrate the same relationship between ordered and random phenomena found on a psychological level. On a microscopic level, it is expected that random neurophysiological processes would give rise to ordered patterns of neurophysiological activity on a macroscopic level

Algorithmic Randomness as Foundation of Inductive Reasoning and Artificial Intelligence

This article is a brief personal account of the past, present, and future of algorithmic randomness, emphasizing its role in inductive inference and artificial intelligence. It is written for a general audience interested in science and philosophy. Intuitively, randomness is a lack of order or predictability. If randomness is the opposite of determinism, then algorithmic randomness is the opposite of computability. Besides many other things, these concepts have been used to quantify Ockham's razor, solve the induction problem, and define intelligence.Comment: 9 LaTeX page

Optimizing Memory-Bounded Controllers for Decentralized POMDPs

We present a memory-bounded optimization approach for solving infinite-horizon decentralized POMDPs. Policies for each agent are represented by stochastic finite state controllers. We formulate the problem of optimizing these policies as a nonlinear program, leveraging powerful existing nonlinear optimization techniques for solving the problem. While existing solvers only guarantee locally optimal solutions, we show that our formulation produces higher quality controllers than the state-of-the-art approach. We also incorporate a shared source of randomness in the form of a correlation device to further increase solution quality with only a limited increase in space and time. Our experimental results show that nonlinear optimization can be used to provide high quality, concise solutions to decentralized decision problems under uncertainty.Comment: Appears in Proceedings of the Twenty-Third Conference on Uncertainty in Artificial Intelligence (UAI2007

Handling uncertainties in modelling manufacturing processes with hybrid swarm intelligence

Seldom has research regarding manufacturing process modelling considered the two common types ofuncertainties which are caused by randomness as in material properties and by fuzziness as in the inexact knowledge in manufacturing processes. Accuracies of process models can be downgraded if these uncertainties are ignored in the development of process models. In this paper, a hybrid swarm intelligence algorithm for developing process models which intends to achieve significant accuracies for manufacturing process modelling by addressing these two uncertainties is proposed. The hybrid swarm intelligence algorithm first applies the mechanism of particle swarm optimisation to generate structures of process models in polynomial forms, and then it applies the mechanism of fuzzy least square regression algorithm to determine fuzzy coefficients on polynomials so as to address the two uncertainties, fuzziness and randomness. Apart from addressing the two uncertainties, the common feature in manufacturing processes, nonlinearities between process parameters, which are not inevitable in manufacturing processes, can also be addressed. The effectiveness of the hybrid swarm algorithm is demonstrated by modelling of the solder paste dispensing process