Argo: a system for design by analogy

Huhns, Michael N.; Acosta, R.D.

doi:10.1109/64.21891

Cited by 24 publications

(8 citation statements)

References 17 publications

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“…This is described in (Carbonell, 1983). There may also be reasons to prefer more specific rules (Huhns & Acosta, 1988). However, when no deductive option is available this technique may give an alternative to backtracking or failure.…”

Section: Discussionmentioning

confidence: 99%

“…This transfer of knowledge between situations can be achieved by derivational analogy (Carbonell, 1983). Analogy has been used to map different situations to one another, in order to apply prior knowledge to a new situation (Huhns & Acosta, 1988); thereby performing transfer learning. This paper aims to use EBL and analogy in order to generate useful control rules for hardware which is outside the design of the controller.…”

Section: Analogymentioning

confidence: 99%

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Explanation-based learning with analogy for impasse resolution

Timperley

Mokhtar

Bellaby

et al. 2016

Expert Systems with Applications

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This paper proposes an algorithm for the inclusion of analogy into Explanation-Based Learning (EBL). Analogy can be used when an impasse is reached to extend the deductive closure of EBL's domain theory. This enables the generation of control laws, via EBL, for hardware which is not catered for in the domain theory. This advantage addresses a problem which represents a dearth in the current literature. Integrated Modular Avionics (IMA) literature has thus far been concerned with the architectural considerations. This paper seeks to address the impact of hardware changes on the controllers within an IMA architecture. An algorithm is proposed and applied to control an aviation platform with an incomplete domain theory. Control rules are generated when no deductive explanations are possible, which still reflect the intent of the domain theory.

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Section: Discussionmentioning

confidence: 99%

Section: Analogymentioning

confidence: 99%

Explanation-based learning with analogy for impasse resolution

Timperley

Mokhtar

Bellaby

et al. 2016

Expert Systems with Applications

View full text Add to dashboard Cite

show abstract

“…Analogies could be used to increase understanding of a novel design by allowing the designer to map previous experience onto the new device. There are many other uses for analogy in design, and there has been some work on models of design by analogy (Bhatta and Goel 1996, Howe et al 1986, Huhns and Acosta 1988. Other methods of transfer besides case-based reasoning and analogy have also been explored.…”

Section: Introductionmentioning

confidence: 99%

Learning from design experience in an agent-based design system

2004

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Abstract.A simple learning mechanism was added to an agent-based computational design system to see if it could then transfer knowledge across problems. An existing system, A-Design, was enhanced by giving it the ability to store useful design knowledge in a memory store so that this knowledge could be used in new design problems. Three electromechanical design problems were used to evaluate this new learning mechanism, and results indicate that this simple learning mechanism is successful at transferring design knowledge to new problems with some limitations.

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“…LEAP records the actions of the designer, generates proofs (or explanations) that those actions satisfy the design constraints, and then generalizes the proofs to create the conditions for new design rules. This idea has been further extended to acquiring generalized design plans in the ARGO system [18].…”

Section: Fig 42 -General Principles Of Designmentioning

confidence: 99%

The role of learning in logic synthesis

Kipps¹,

Gajski²

[Proceedings 1989] IEEE International Workshop on Tools for Artificial Intelligence

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Irvine, C A 92717 A B S T R A C TThe goal of logic synthesis is to obtain high-quality designs from specifications. Current approaches to logic synthesis often tradeoff design quality for technology independence. In this paper, we present a model of logic synthesis that uses technology-specific design rules and extends rule-based search to functional decomposition and technology mapping. While this model improves design quality by taking advantage of the target technology, it is not robust to technology changes. To improve robustness, we augment the model with two learning components: one for acquiring rules that make use of physical cells in a technology library, and another for acquiring rules that make use of appropriate design styles. These components are related to work in learning of macrooperators and explanation-based learning.

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Argo: a system for design by analogy

Cited by 24 publications

References 17 publications

Explanation-based learning with analogy for impasse resolution

Explanation-based learning with analogy for impasse resolution

Learning from design experience in an agent-based design system

The role of learning in logic synthesis

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