(CMAC) [1, 2] is a neural network that models the structure and function of the part of the brain known as the cerebellum. The cerebellum provides precise coordination of motor control for such body parts as the eyes, arms, fingers, legs, and wings. It stores and retrieves information required to control thousands of muscles in producing coordinated behavior as a function of time. CMAC was designed to provide this kind of motor control for robotic manipulators. CMAC is a kind of memory, or table look-up mechanism, that is capable of learning motor behavior. It exhibits properties such as generalization, learning interference, discrimination, and forgetting that are characteristic of motor learning in biological creatures. In a biological motor system, the drive signal for each

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...

A good software architecture facilitates application system development, promotes achievement of functional requirements, and supports system reconfiguration. We present a domain-specific software architecture (DSSA) that we have developed for a large application domain of adaptive intelligent systems (AISs). The DSSA provides: (a) an AIS reference architecture designed to meet the functional requirements shared by applications in this domain, (b) principles for decomposing expertise into highly reusable components, and (c) an application configuration method for selecting relevant components from a library and automatically configuring instances of those components in an instance of the architecture. The AIS reference architecture incorporates features of layered, pipe and filter, and blackboard style architectures. We describe three studies demonstrating the utility of our architecture in the sub-domain of mobile office robots and identify software engineering principles embodied in ...

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Human-robot interaction is becoming an increasingly important research area. In this paper, we present our work on designing a human-robot system with adjustable autonomy and describe not only the prototype interface but also the corresponding robot behaviors. In our approach, we grant the human meta-level control over the level of robot autonomy, but we allow the robot a varying amount of self-direction with each level. Within this framework of adjustable autonomy, we explore appropriate interface concepts for controlling multiple robots from multiple platforms.

The lure of using motion vision as a fundamental element in the perception of space drives this effort to use flow features as the sole cues for robot mobility.

This chapter describes one basic model of associative memory, called the sparse distributed memory, and relates it to other models and circuits: to ordinary computer memory, to correlation-matrix memories, to feed-forward artificial neural nets, to neural circuits in the brain, and to associative-memory models of the cerebellum.

Abstract — Robots that work alongside us in our homes and workplaces could extend the time an elderly person can live at home, provide physical assistance to a worker on an assembly line, or help with household chores. In order to assist us in these ways, robots will need to successfully perform manipulation tasks within human environments. Human environments present special challenges for robot manipulation since they are complex, dynamic, uncontrolled, and difficult to perceive reliably. In this paper we present a behavior-based control system that enables a humanoid robot, Domo, to help a person place objects on a shelf. Domo is able to physically locate the shelf, socially cue a person to hand it an object, grasp the object that has been handed to it, transfer the object to the hand that is closest to the shelf, and place the object on the shelf. We use this behavior-based control system to illustrate three themes that characterize our approach to manipulation in human environments. The first theme, cooperative manipulation, refers to the advantages that can be gained by having the robot work with a person to cooperatively perform manipulation tasks. The second theme, task relevant features, emphasizes the benefits of carefully selecting the aspects of the world that are to be perceived and acted upon during a manipulation task. The third theme, let the body do the thinking, encompasses several ways in which a robot can use its body to simplify manipulation tasks. 1 Fig. 1. The humanoid robot Domo used in this paper. I.

This work describes a control architecture based on a hierarchical classifier system. This architecture, which uses both reactive and planning rules, implements a motivationally autonomous animat that chooses the actions it will perform according to the expected consequences of the alternatives. The adaptive faculties of this animat are illustrated through various examples.

Real robots with real sensors are not omniscient. When a robot's next course of action depends on information that is hidden from the sensors because of problems such as occlusion, restricted range, bounded field of view and limited attention, we say the robot suffers from the hidden state problem. State identification techniques use history information to uncover hidden state. Some previous approaches to encoding history include: finite state machines [12, 28], recurrent neural networks [25] and genetic programming with indexed memory [49]. A chief disadvantage of all these techniques is their long training time. This paper presents instance-based state identification, a new approach to reinforcement learning with state identification that learns with much fewer training steps. Noting that learning with history and learning in continuous spaces both share the property that they begin without knowing the granularity of the state space, the approach applies instance-based (or "memory-ba...