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All intelligence relies on search --- for example, the search for an intelligent agent's next action. Search is only likely to succeed in resource-bounded agents if they have already been biased towards finding the right answer. In artificial agents, the primary source of bias is engineering. This dissertation

This paper discusses the phenomenological dimension of social understanding. The author’s general hypothesis is that complex forms of social unders standing that biological agents especially humans show are based on two s. mechanisms: 1 the bodily, experiential dynamics of emphatic resonance s. and 2 the biographic reconstruction of a communication situation. The latter requires the agent’s bodily experiences as the point of reference for the reconstruction process. This hypothesis is derived from discussions in philosophy, natural sciences, and cognitive science on the social embodiment of cognition and understanding. Evidence comes from studies on social cognition in primates, infants, and autistic people that are interpreted in terms of the ``mind-experiencing’ ’ hypothesis. The second part of the The writing of this paper was supported by an HCMr TMR research grant. Thanks to Erich Prem and an anonymous reviewer for their comments and suggestions, which helped me to improve a previous version of this paper. I am grateful to both the AI-Lab at GMD in Germany and the VUB AI-Lab in Belgium for giving me an environment for doing my research on social agents. The seesaw scenario was part of a student project s Claus Divossen, Susanne Jucknath, Michael Savels. in collaboration with the University of Bonn, Germany. I thank Sanjida O’Connell for discussions on empathy and theory of mind. Armin Deierling provided me with information about autism resources and discussed with me the question of how people with autism probably perceive the world.

Hierarchical organization has become an unfashionable model of intelligent control within some communities of both natural and artificial intelligence. What has replaced it are models based on parallel distributed processes, both neural and behavior based, or dynamical systems theory, which denies modularity, let alone rigorous structure. In this paper we present experimental results demonstrating an artificial reactive hierarchybased system that outperforms fully parallel systems in a highly dynamic environment with a large number of conflicting goals. This work is conducted in Tyrrell’s (1993) Simulated Environment and can be seen as an extension of his work on comparing action selection mechanisms. We observe that the hierarchical strategy has also been well demonstrated in nature. We argue that, for complex intelligences, preserving full reactivity may not be worth the cost in terms of the complexity of action selection.

not be interpreted as representing the official policies, either expressed or implied, of the ONR or the U.S. government. Keywords: ACT-R, cognitive arithmetic, Bayesian learning, activation spreading, dynamical systems, parameter analysis, power law, machine learning, hybrid systems. Cognitive arithmetic, the study of the mental representation of numbers and arithmetic facts and the processes that create, access and manipulate them, offers a unique window into human cognition. Unlike traditional Artificial Intelligence (AI) tasks, cognitive arithmetic is trivial for computers but requires years of formal training for humans to master. Understanding the basic assumptions of the human cognitive system which make such a simple and well-understood task so challenging might in turn help us understand how humans perform other, more complex tasks and engineer systems to emulate them. The wealth of psychological data on every aspect of human performance of arithmetic makes precise computational modeling of the detailed error

Abstract This paper outlines an original interactivist-constructivist (I-C) approach to modeling intelligence and learning as a dynamical embodied form of adaptiveness and explores some applications of I-C to understanding the way cognitive learning is realised in the brain. Two key ideas for conceptualising intelligence within this framework are developed. These are: (i) intelligence is centrally concerned with the capacity for coherent, contextsensitive, self-directed management of interaction, (ii) the primary model for cognitive learning is anticipative skill construction. Self-directedness is a capacity for integrative process modulation which allows a system to ‘steer ’ itself through its world by anticipatively matching its own viability requirements to interaction with its environment. Because the adaptive interaction processes required of intelligent systems are too complex for effective action to be prespecified (e.g. genetically) learning is an important component of intelligence. A model of self-directed anticipative learning (SDAL) is formulated based on interactive skill construction, and argued to constitute a central constructivist process involved in cognitive development. SDAL illuminates the capacity of intelligent learners to start with the vague, poorly defined, problems typically posed in realistic learning situations and progressively refine them, transforming them into problems with sufficient structure to guide the construction of a solution. Finally, some of the implications of I-C for modeling of the neuronal basis of intelligence and learning are explored; in particular, Quartz and Sejnowski’s recent neural constructivism (NC) paradigm, enriched by Montague and Sejnowski’s dopaminergic model of anticipative-predictive neural learning, is assessed as a promising, but incomplete, contribution to this approach. The paper concludes with a four-fold reflection on the divergence in cognitive modeling philosophy between the I-C and the traditional computational information processing (CIP) approaches. 1.

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The search for chaotic patterns has occupied numerous investigators in neuroscience, as in many other fields of science. Their results and main conclusions are reviewed in the light of the most recent criteria that need to be satisfied since the first descriptions of the surrogate strategy. The methods used in each of these studies have almost invariably combined the analysis of experimental data with simulations using formal models, often based on modified Huxley and Hodgkin equations and/or of the Hindmarsh and Rose models of bursting neurons. Due to technical limitations, the results of these simulations have prevailed over experimental ones in studies on the nonlinear properties of large cortical networks and higher brain functions. Yet, and although a convincing proof of chaos (as defined mathematically) has only been obtained at the level of axons, of single and coupled cells, convergent results can be interpreted as compatible with the notion that signals in the brain are distributed according to chaotic patterns at all levels of its various forms of hierarchy. This chronological account of the main landmarks of nonlinear neurosciences follows an earlier publication [Faure, Korn, C. R. Acad. Sci. Paris, Ser. III 324 (2001) 773–793] that was focused on the basic concepts of nonlinear dynamics and methods of investigations which allow chaotic processes to be distinguished from stochastic ones and on the rationale for envisioning their control using external perturbations. Here we present the data and main arguments that support the existence of chaos at all levels from the simplest to the most complex forms of organization of the nervous system.

One of the defining features of intelligent behaviour is the ordering of individual expressed actions into coherent, apparently rational patterns. Psychology has long assumed that hierarchical and sequential structures internal to the intelligent agent underlie this expression. Recently these assumptions have been challenged by claims that behaviour controlled by such structures is necessarily rigid, brittle, and incapable of reacting quickly and opportunistically to changes in the environment (Hendriks-Jansen 1996, Goldfield 1995, Brooks 1991a). This dissertation is intended to support the hypothesis that sequential and hierarchical structures are necessary to intelligent behaviour, and to refute the above claims of their impracticality. Three forms of supporting evidence are provided: • a demonstration in the form of experimental results in two domains that structured intelligence can lead to robust and reactive behaviour, • a review of recent research results and paradigmatic trends within artificial intelligence, and • a similar examination of related research in natural intelligence.

A framework for understanding and exploiting embodiment is presented which is not dependent on any specific ontological context. This framework is founded on a new definition of embodiment, based on the relational dynamics that exist between biological organisms and their environments, and inspired by the structural dynamics of the Escherichia coli bacteria. Recognition is given to the role played by physically instantiated bodies, but in such a way that this can be meaningfully abstracted within the constraints implied by the term 'embodiment', and applied in a variety of operational contexts. This is supported by ongoing experimental work in which the relational dynamics that exist between E. coli and its environment are applied in a variety of software environments, using Cellular Automata (CA) with artificial 'sensory' and 'effector' surfaces, producing qualitatively similar 'chemotaxic' behaviours in a variety of operational domains.