An investigation of nonlinear mathematical models of dynamics has led to the selection of characteristics that could be useful for distinguishing mathematically between the behaviors of (1) intelligent or living systems and (2) nonliving systems. As contemplated here, an intelligent or living system could range from a natural or artificial single-cell organism at one extreme to the whole of human society at the other extreme, whereas a nonliving system could be, for example, a collection of interacting particles or mechanisms. Among other findings, the investigation has revealed that living systems can be characterized by nonlinear evolution of probability distributions over different possible choices of the next steps in their motions.
One of the main challenges in mathematical modeling of living systems is to distinguish between random walks of purely physical origin (for instance, Brownian motions) and those of biological origin. Following a line of reasoning from prior research, it was assumed, in this investigation, that a biological random walk can be represented by a nonlinear mathematical model that represents coupled mental/motor dynamics incorporating the psychological concept of reflection or self-image. The nonlinear dynamics impart the lifelike ability to behave in ways and to exhibit patterns that depart from thermodynamic equilibrium. Reflection has traditionally been recognized as a basic element of intelligence.
In this investigation, the motor dynamics were represented by (1) a generator of stochastic processes representing the motor dynamics of a nonlinear one-dimensional random walk plus (2) a model of the corresponding evolution of the dynamics in probability space. Associated with the probabilistic model of the motor dynamics was a nonlinear version of the Fokker-Planck equation representing the flows of information in probability space: this model was taken to represent both the mental dynamics and a probabilistic self-image of the dynamic system. It was postulated that if the dynamic system "possesses" its self-image, then it can predict future expected values of its parameters and change the expectations if they are not consistent with what is observed.
It was then shown that a living system according to this model can predict the future in terms of probabilities, because of the smoothness of the evolution in probability space (such smoothness does not exist in physical space because of irregularities of a random walk). This ability to predict increases chances for survival and can be considered a basic component of intelligence. It was shown that the coupled motor/mental dynamics can simulate such lifelike phenomena as emerging self-organization, decision-making based on "common sense," predator/prey evolutionary games, and a collective brain. Both the mental and motor dynamics can be implemented by hardware (e.g., neural networks or cellular automata), thereby enabling artificially intelligent systems to exhibit such lifelike phenomena.
This work was done by Michail Zak of Caltech for NASA's Jet Propulsion Laboratory. For further information, access the Technical Support Package (TSP) free on-line at www.nasatech.com/tsp under the Information Sciences category.
NPO-21037
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Characteristics of Dynamics of Intelligent Systems
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Overview
The document is a NASA Technical Support Package detailing the "Characteristics of Dynamics of Intelligent Systems," prepared under the sponsorship of NASA and associated with the Jet Propulsion Laboratory (JPL) at the California Institute of Technology. It is part of the JPL New Technology Report (NTR) NPO-21037, authored by inventor Michail Zak, and was published on December 1, 2002.
The primary focus of the document is on the development of a novel approach to the problem of life-nonlife discrimination, a challenge that has implications in various fields, including astrobiology and artificial intelligence. The motivation for this research stems from the need to differentiate between living and non-living systems, a problem that has been articulated by the Institute of Astrology.
The document outlines the novelty of the work, emphasizing the formulation of dynamical invariants that characterize the behavior of intelligent systems. These invariants are crucial for understanding the nonlinear evolution of probability distributions that govern the decision-making processes of intelligent living systems. The research posits that intelligent systems exhibit distinct patterns in their motion choices, which can be analyzed to discern their living status.
In terms of technical disclosure, the document is structured to provide a clear understanding of the problem, the motivation behind the research, and the proposed solution. It highlights that the intelligent living systems are marked by their nonlinear evolution, which differentiates them from non-living systems. This insight is significant as it offers a new perspective on how to approach the classification of systems based on their dynamical characteristics.
The document also includes a notice stating that references to specific commercial products or services do not imply endorsement by the U.S. Government or JPL. It clarifies that the information contained within is provided without any warranty regarding its freedom from privately owned rights.
Overall, this technical support package serves as a foundational report on the dynamics of intelligent systems, presenting innovative methodologies for addressing complex problems in life-nonlife discrimination, and contributing to the broader understanding of intelligent behavior in both natural and artificial contexts.
