A half century of progress towards a unified neural theory of mind and brain with applications to autonomous adaptive agents and mental disorders

Invited article for the book Artificial Intelligence in the Age of Neural Networks and Brain Computing R. Kozma, C. Alippi, Y. Choe, and F. C. Morabito, Eds. Cambridge, MA: Academic PressThis article surveys some of the main design principles, mechanisms, circuits, and architectures that have been discovered during a half century of systematic research aimed at developing a unified theory that links mind and brain, and shows how psychological functions arise as emergent properties of brain mechanisms. The article describes a theoretical method that has enabled such a theory to be developed in stages by carrying out a kind of conceptual evolution. It also describes revolutionary computational paradigms like Complementary Computing and Laminar Computing that constrain the kind of unified theory that can describe the autonomous adaptive intelligence that emerges from advanced brains. Adaptive Resonance Theory, or ART, is one of the core models that has been discovered in this way. ART proposes how advanced brains learn to attend, recognize, and predict objects and events in a changing world that is filled with unexpected events. ART is not, however, a “theory of everything” if only because, due to Complementary Computing, different matching and learning laws tend to support perception and cognition on the one hand, and spatial representation and action on the other. The article mentions why a theory of this kind may be useful in the design of autonomous adaptive agents in engineering and technology. It also notes how the theory has led to new mechanistic insights about mental disorders such as autism, medial temporal amnesia, Alzheimer’s disease, and schizophrenia, along with mechanistically informed proposals about how their symptoms may be ameliorated

A risk driven state merging algorithm for learning DFAs

When humans efficiently infer complex functions from a relatively few but well- chosen examples, something beyond exhaustive search must probably be at work. Different heuristics are often made use of during this learning process in order to efficiently infer target functions. Our current research focuses on different heuristics through which regular grammars can be efficiently inferred from a minimal amount of examples. A brief introduction to the theory of grammatical inference is given, followed by a brief discussion of the current state of the art in automata learning and methods currently under development which we believe can improve automata learning when using sparse data.peer-reviewe

Non-monotonic search strategies for grammatical inference

Advances in DFA learning algorithms have been relatively slow over the past few years. After the introduction of Rodney Price’s EDSM heuristic [4], pushing the limits of DFA learning appears to be a very difficult task. The S-EDSM heuristic proposed in [6, 1], manages to improve slightly on what EDSM can do. In this paper we outline our current research results, and propose the use of non-monotonic search strategies in order to improve the success rate of DFA inference.peer-reviewe

Automatic Vehicle Checking Agent (VCA)

A definition of intelligence is given in terms of performance that can be quantitatively measured. In this study, we have presented a conceptual model of Intelligent Agent System for Automatic Vehicle Checking Agent (VCA). To achieve this goal, we have introduced several kinds of agents that exhibit intelligent features. These are the Management agent, internal agent, External Agent, Watcher agent and Report agent. Metrics and measurements are suggested for evaluating the performance of Automatic Vehicle Checking Agent (VCA). Calibrate data and test facilities are suggested to facilitate the development of intelligent systems. Keywords: VCA, Agents

Agent-based Simulation Analysis for Network Formation

In this paper, we conduct agent-based simulation experiments for network formation analysis. In the published papers, Bala and Goyal (2000) have constructed a mathematical model leading a star network to be strict Nash equilibrium. However, Berninghaus et al. (2007) have conducted the laboratory experiments using human subjects basing on the mathematical model, and the result of the experiments indicates that human subjects do not always make decision just as the mathematical model predicted. In this paper, we propose a simulation model using the adaptive artificial agents to clarify the reason of the deviation from the mathematical predictions

Intelligent Agents

A search on Google for the keywords intelligent agents\u27 will return more than 330,000 hits; multi-agent returns almost double that amount. Over 5,000 citations appear on www.citeseer.com. What is agent technology and what has led to its enormous popularity in both the academic and commercial worlds? Agent-based system technology offers a new paradigm for designing and implementing software systems. The objective of this tutorial is to provide an overview of agents, intelligent agents and multi-agent systems, covering such areas as: 1. what an agent is, its origins and what it does, 2. how intelligence is defined for and differentiates an intelligent agent from an agent, 3. how multi-agent systems coordinate agents with competing goals to achieve a meaningful result, and 4. how an agent differs from an object of a class or an expert system. Examples are presented of academic and commercial applications that employ agent technology. The potential pitfalls of agent development and agent usage are discussed

Smart grids as distributed learning control

The topic of smart grids has received a lot of attention but from a scientific point of view it is a highly imprecise concept. This paper attempts to describe what could ultimately work as a control process to fulfill the aims usually stated for such grids without throwing away some important principles established by the pioneers in power system control. In modern terms, we need distributed (or multi-agent) learning control which is suggested to work with a certain consensus mechanism which appears to leave room for achieving cyber-physical security, robustness and performance goals. © 2012 IEEE.published_or_final_versio