Intelligent control of complex materials processes

Pardee, W. J.; Shaff, Michael A.; Hayes-Roth, Barbara

doi:10.1017/s0890060400002249

Cited by 12 publications

(3 citation statements)

References 6 publications

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“…Practicism|System science: integration of AI with other software tools and hardware (Pardee et al, 1990), or robotics (Brooks, 1991).…”

Section: Research Perspectives/heuristics: Sources Of Hypothesesmentioning

confidence: 99%

Layered models of research methodologies

Reich

1994

AIEDAM

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The status of research methodology employed by studies on the application of AI techniques to solving problems in engineering design, analysis, and manufacturing is poor. There may be many reasons for this status, including: unfortunate heritage from AI, poor educational system, and researchers’ sloppiness. Understanding this status is a prerequisite for improvement. The study of research methodology can promote such understanding, but, most importantly, it can assist in improving the situation. Concepts from the philosophy of science are introduced, and models of world views of science are built on them. These world views are combined with research heuristics or research perspectives and criteria for evaluating research to create a layered model of research methodology. This layered model can serve to organize and facilitate a better understanding of future studies of research methodologies. Many of the issues involved in the study of AI and AIEDAM research methodology using this layered model are discussed.

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“…Practicism|System science: integration of AI with other software tools and hardware (Pardee et al, 1990), or robotics (Brooks, 1991).…”

Section: Research Perspectives/heuristics: Sources Of Hypothesesmentioning

confidence: 99%

Layered models of research methodologies

Reich

1994

AIEDAM

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“…Each one has been demonstrated on two or three simple, but realistic scenarios. A similar agent has been developed to monitor materials processing [Pardee, et al, 1990]. These applications demonstrate the generality of our agent architecture across diverse domains within the AIS monitoring niche.…”

Section: Current Status Of Experimental Agentsmentioning

confidence: 95%

An Architecture for Adaptive Intelligent Systems.

Hayes-Roth¹

1993

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We identify a class of niches to be occupied by 'adaptive intelligent sy^ms (AISs)'. In contrast with niches occupied by typ ical 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. We have designed and implemented an agent architecture that supports all of different kinds o adaptation by exploiting a single underlying t heoretical concept: An agent dynamicallA co n structs explicit control plans to guide its choices among situation-triggered behaviors. We illustrate the architecture and its support for adaptation with examples from Guardian, an experimental agent for ICU monitoring. AbstractOur 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 Al

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“…We have experimented with our DSSA in three major sub-classes of AIS applications: (a) monitoring systems in four specific domains: intensive care [15,20,22], materials processing [29], semiconductor manufacturing [27], and power plant operations [31]; (b) layout design systems in two specific domains: protein structure modeling [17,18] and construction site layout [33]; and (c) autonomous office robots in two specific domains: office surveillance and office delivery [19]. In section 3, we present three studies and their results from our work on office robots to demonstrate the utility of the AIS DSSA.…”

Section: Introductionmentioning

confidence: 99%

A domain-specific software architecture for a class of intelligent patient monitoring agents

Hayes-Roth¹

1996

Journal of Experimental & Theoretical Artificial Intelligen

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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 the architecture.

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Intelligent control of complex materials processes

Cited by 12 publications

References 6 publications

Layered models of research methodologies

Layered models of research methodologies

An Architecture for Adaptive Intelligent Systems.

A domain-specific software architecture for a class of intelligent patient monitoring agents

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