To describe phenomena that occur at different time scales, computational models of the brain must necessarily incorporate different levels of abstraction. We argue that at time scales of approximately one-third of a second, orienting movements of the body play a crucial role in cognition and form a useful computational level, termed the embodiment level . At this level, the constraints of the body determine the nature of cognitive operations, since the natural sequentiality of body movements can be matched to the natural computational economies of sequential decision systems. The way this is done is through a system of implicit reference termed deictic, whereby pointing movements are used to bind objects in the world to cognitive programs. We show how deictic bindings enable the solution of natural tasks and argue that one of the central features of cognition, working memory, can be related to moment-by-moment dispositions of body features such as eye movements and hand movements. Keyw...

We describe a project to capitalize on newly available levels of computational resources in order to understand human cognition. We are building an integrated physical system including vision, sound input and output, and dextrous manipulation, all controlled by a continuously operating large scale parallel MIMD computer. The resulting system will learn to "think " by building on its bodily experiences to accomplish progressively more abstract tasks. Past experience suggests that in attempting to build such an integrated system we will have to fundamentally change the way artificial intelligence, cognitive science, linguistics, and philosophy think about the organization of intelligence. We expect to be able to better reconcile the theories that will be developed with current work in neuroscience.

This paper describes Minerva, an interactive tour-guide robot that was successfully deployed in a Smithsonian museum. Minerva's software is pervasively probabilistic, relying on explicit representations of uncertainty in perception and control. This article describes

Abstract. To explore issues of developmental structure, physical embodiment, integration of multiple sensory and motor systems, and social interaction, we have constructed an upper-torso humanoid robot called Cog. The robot has twenty-one degrees of freedom and a variety of sensory systems, including visual, auditory, vestibular, kinesthetic, and tactile senses. This chapter gives a background on the methodology that we have used in our investigations, highlights the research issues that have been raised during this project, and provides a summary of both the current state of the project and our long-term goals. We report on a variety of implemented visual-motor routines (smooth-pursuit tracking, saccades, binocular vergence, and vestibular-ocular and opto-kinetic reflexes), orientation behaviors, motor control techniques, and social behaviors (pointing to a visual target, recognizing joint attention through face and eye finding, imitation of head nods, and regulating interaction through expressive feedback). We further outline a number of areas for future research that will be necessary to build a complete embodied system. 1

this paper, I will argue that, contrary to these challenges, planning deserves its central place on the AI map. I will claim that intelligent agents are planning agents, and that philosophical and commonsense psychological theorizing about the process of planning can provide useful insights into the question of agent design. The theories I have in mind are not restricted to The Uses of Plans 3 how agents can form plans. Much of my research has concerned the ways in which intelligent agents use their plans. I will describe some of that research, and will argue that plans are used not only to guide action, but also to control reasoning and to enable inter-agent coordination. These uses of plans make possible intelligent behavior in complex, dynamic, multiagent environments. 2 Planning We can begin by asking what exactly we mean by "planning". For many years, planning had a quite specific meaning in AI: it was the process of formulating a program of action to achieve some specified goal. You gave a planning system a description of initial conditions and a goal, and it produced a plan of action whose execution in a state satisfying the initial conditions was guaranteed to result in a state satisfying the goal. These plans were akin to recipes for achieving the goal. Your goal might be to have a chocolate cake. In the initial state, you might have eggs, milk, and chocolate, a pan and a working oven. In these conditions, a valid plan might be to go the store to buy some flour, return home, preheat the oven, mix the ingredients, pour the mixture into the pan, and put it in the oven for 45 minutes. Traditional AI planning systems like STRIPS [22], NOAH [63], and SIPE [71], were designed to construct just this kind of plan---except usually the goal was something like a tower o...

We describe a computer program which is able to estimate the tempo and the times of musical beats in expressively performed music. The input data may be either digital audio or a symbolic representation of music such as MIDI. The data is processed off-line to detect the salient rhythmic events and the timing of these events is analysed to generate hypotheses of the tempo at various metrical levels. Based on these tempo hypotheses, a multiple hypothesis search nds the sequence of beat times which has the best fit to the rhythmic events. We show that estimating the perceptual salience of rhythmic events significantly improves the results. No prior knowledge of the tempo, meter or musical style is assumed; all required information is derived from the data. Results are presented for a range of different musical styles, including classical, jazz, and popular works with a variety of tempi and meters. The system calculates the tempo correctly in most cases, the most common error being a doubling or halving of the tempo. The calculation of beat times is also robust. When errors are made concerning the phase of the beat, the system recovers quickly to resume correct beat tracking, despite the fact that there is no high level musical knowledge encoded in the system.

Our goal is to understand and build comprehensive agents that function effectively in challenging niches. In particular, we identify a class of niches to be occupied by "adaptive intelligent systems (AISs)." In contrast with niches occupied by typical AI agents, AIS niches present situations that vary dynamically along several key dimensions: different combinations of required tasks, different configurations of available resources, contextual conditions ranging from benign to stressful, and different performance criteria. We present a small class hierarchy of AIS niches that exhibit these dimensions of variability and describe a particular AIS niche, ICU (intensive care unit) patient monitoring, which we use for illustration throughout the paper. To function effectively throughout the range of situations presented by an AIS niche, an agent must be highly adaptive. In contrast with the rather stereotypic behavior of typical AI agents, an AIS must adapt several key aspects of its behavio...

The pitfalls of naive robot simulations have been recognised for areas such as evolutionary robotics. It has been suggested that carefully validated ispell slides.tex simulations with a proper treatment of noise may overcome these problems. This paper reports the results of experiments intended to test some of these claims. A simulation was constructed of a two-wheeled Khepera robot with IR and ambient light sensors. This included detailed mathematical models of the robot-environment interaction dynamics with empirically determined parameters. Artificial evolution was used to develop recurrent dynamical network controllers for the simulated robot, for obstacle-avoidance and light-seeking tasks, using different levels of noise in the simulation. The evolved controllers were down-loaded onto the real robot and the correspondence between behaviour in simulation and in reality was tested. The level of correspondence varied according to how much noise was used in the simulation, with very g...

Behavior-oriented AI is a scientific discipline that studies how behavior of agents emerges and becomes intelligent and adaptive. Success of the field is defined in terms of success in building physical agents that are capable of maximising their own self-preservation in interaction with a dynamically changing environment. The paper addresses this artificial life route towards artificial intelligence and reviews some of the results obtained so far. 1 Official reference: Steels, L. (1994) The artificial life roots of artificial intelligence. Artificial Life Journal, Vol 1,1. MIT Press, Cambridge. 1 Introduction For several decades, the field of Artificial Intelligence has been pursuing the study of intelligent behavior using the methodology of the artificial [104]. But the focus of this field, and hence the successes, have mostly been on higher order cognitive activities such as expert problem solving. The inspiration for AI theories has mostly come from logic and the cognitive...

This technical definition will only make sense toe reader by Ch. 4, once goals and management processes have been described. All that matters forrs section is that a difference between goals and perturbance be noted by the reader. Astate perturbance is not a goal, but it arises out of the processing of goals. In Ch. 7, arelation00 perturbance and "emotion" is discussed. 43 . Sloman says of certain moods that they are "persistent states with dispositional power to color and modify a host of other states and processes. Such moodscan39061-6 be caused by cognitive events with semantic content, though they need not be.[...]0-64000 their control function does not require specific semantic content, though theycan0371-62 cognitive processes that do involve semantic content." (Sloman, 1992b Section 6).A 39642 view is taken in (Oatley, 1992). To be more precise, moods are temporary control stateswhich9881-5 the prominence of some motivators while decreasing others. In particular, they affectthe 41330-5 that certain "goal generators" are triggered. Moreover, moods affect the valenceofce 39476 evaluations, and the likelihood of affective evaluations (perhaps by modifying thresholdsofsholds 42 that trigger evaluations). It is not yet clear whether moods as defined here are9531 - or whether they merely emerge as side-effects of functional processes. . A reflex is a ballistic form of behaviour that can be specified by a narrow setw rules based on input integration and a narrow amount of internal state. There aretwo0981 of reflexes: simple reflexes and fixed action patterns. A simple reflex involves oneaction,-43000 a fixed action pattern involves a collection of actions. Usually, at most only asmall-4120 of perceptual feedback influences reflex action. This would require a definit...