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...

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 ...

Present work addresses the guidelines that have been followed to construct Basic Behavioral Agents for visually guided navigation tasks within the framework of a Hierarchical Architecture. Visual and motor interactions are also described within this generic framework that allows for an incremental adaptation of knowledge, to be reused in tasks of ever increasing complexity. Basic Locomotion Agents as, Stop&Backward, Avoid, and Forward are implemented as Fuzzy knowledge- based systems to embody the uncertainty and imprecision inherent to real system-environments. Basic Visual Agents as, Saccadic, Contour, and Center have been developed under a space-variant representation in an anthropomorphic approach. Coordination of Basic Agents has been demonstrated with a caterpillar type mobile robot in tasks related to qualitative descriptions of visual stimuli.

An "intelligent agent" is a versatile and adaptive computer system. It performs diverse cognitive and physical behaviors in its efforts to achieve multiple goals in a dynamic, uncertain task environment. In this paper, we address the question: How should plans influence the cognitive and physical behavior of intelligent agents? In contrast to the well-known model of plans as executable programs, we propose that intelligent agents can make better use of plans that describe their intended behavior. This kind of model has been discussed by other researchers, but it has not been operationalized. We instantiate this model with operational definitions of plans, planning, and plan following. We present an agent architecture for using such plans to guide an agent's cognitive and physical behaviors. Experimental results from an office robot demonstrate important capabilities engendered by our approach: (a) coordination of diverse cognitive and physical behaviors to achieve multiple goals; (b) r...

There were many people who aided me during the completion of this research. I would first like to thank Dr. Donald H. Liles, the chair of my disseration committee, for his guidance and support with this research. He provided the vision of enterprise engineering and modeling which set the framework for this work. His encouraged me to stretch and to not settlle for anything less than what I was capable of. Special thanks also goes to Dr. G. T. Stevens, who served on my committee and whose counsel led me to choose industrial engineering as my field of study. I would also like to thank the other members of my committee: Dr. Brian Huff, Dr. Jamie Rogers, and Dr. R. C. Baker. Thanks also to Dr. Joseph Sarkis, an original member of the committee. I would like to thank my colleagues in the Enterrpise Engineering group at the Automation & Robotics Research Institute for their suggestions and for providing an outlet for many of my ideas. More anything, however, thank you for your firendship. Finally, to the people to whom I owe the most: my family. Thanks to my mother, Emiko Edwards, for always motivating me and encouraging me. To my children Janette, Nicky, and the one still to come and to my wife Theresa, thanks for your support, scarifices, and love. I love you all.

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . x I. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1-1 1.1 Goals and Scope . . . . . . . . . . . . . . . . . . . . . . . . . 1-2 1.2 Organization . . . . . . . . . . . . . . . . . . . . . . . . . . . 1-3 II. Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-1 2.1 Anytime algorithms . . . . . . . . . . . . . . . . . . . . . . . 2-1 2.2 Algorithm Combinations . . . . . . . . . . . . . . . . . . . . 2-3 2.3 Control Theory . . . . . . . . . . . . . . . . . . . . . . . . . . 2-4 2.4 Intelligent Control . . . . . . . . . . . . . . . . . . . . . . . . 2-5 2.5 Bayesian Networks . . . . . . . . . . . . . . . . . . . . . . . . 2-8 2.6 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-10 III. Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-1 3.1 Phase 1: Architecture Development . . . . . . . . . . . . . . 3-1 ...

This dissertation describes CPA, a general-purpose mechanism for expressing and implementing attention policies that control the allocation of resources among sensory processing tasks in a robot or other advanced intelligent system. A wide variety of attention policies can be expressed in this mechanism, which also supports soft real-time constraints on perceptual processing. Intelligent systems can become inundated with data, resulting in perceptual overload and a consequent inability to formulate a timely or appropriate response. Perceptual overload is often modulated by a perceptual attention mechanism that filters and prioritizes incoming data. Most existing attention mechanisms are tailored to the specific task the system is performing. A general-purpose attention mechanism must have a task-independent interface for controlling attention; support a heterogeneous set of sensors; support heterogeneous methods for processing sensor data; and support real-time throughput constraints. The CPA is a general-purpose attention mechanism that supports multimodal perceptual attention.

A new algorithm for the distributed simulation and evaluation of on-line schedules is presented. Generally, on-line scheduling has often been restricted to scheduling activities on a single machine or workcell. The exploratory research reported in this paper expands on-line scheduling to encompass several machines or workcells. Branch and bound search techniques are used in the simulation to reduce the number of simulations simultaneously in execution. The algorithm is applied for the distributed simulation of on-line schedules for a manufacturing example. 1. INTRODUCTION Several obstacles prevent current assembly and manufacturing lines from capitalizing on the promises of complete automation. Among these are the difficulties in scheduling 1 resources and ordering processes such that optimal use is made of the current configuration. Fox and Kempf [FoKe85] distinguish the differences between planning and scheduling and suggest that a distinction is both essential and beneficial. Wh...

This paper describes PalymSys TM-- an extended version of the CLIPS language that is designed to facilitate the implementation of blackboard systems. The paper first describes the general characteristics of blackboards and shows how a control blackboard architecture can be used by AI systems to examine their own behavior and adapt to real-time problem-solving situations by striking a balance between domain and control reasoning. The paper then describes the use of PalymSys TM in the development of a situation assessment subsystem for use aboard Army helicopters. This system performs real-time inferencing about the current battlefield situation using multiple domain blackboards as well as a control blackboard. A description of the control and domain blackboards and their implementation is presented. The paper also describes modifications made to the standard CLIPS 6.02 language in PalymSys TM 2.0. These include: 1. A dynamic Dempster-Shafer belief network whose structure is completely specifiable at run-time in the consequent of a PalymSys TM rule, 2. Extension of the run command including a continuous run feature that enables the system to run even when the agenda is empty, and 3. A built-in communications link that uses shared memory to communicate with other independent processes.

Sophisticated agents operating in open environments must make complex real-time control decisions on scheduling and coordination of domain activities. These decisions are made in the context of limited resources and uncertainty about outcomes of activities.