Configuration Tree Solver: A Technology for Automated Design and Configuration

1992 ◽  
Vol 114 (1) ◽  
pp. 187-195 ◽  
Author(s):  
A. Kott ◽  
G. Agin ◽  
D. Fawcett
Keyword(s):  
Problem Solving ◽  
General Model ◽  
General Class ◽  
Domain Knowledge ◽  
Mechanical Design ◽  
Automated Design ◽  
Automotive Component ◽  
Knowledge Based ◽  
Specific Configuration

Configuration is a process of generating a definitive description of a product that satisfies a set of specified requirements and known constraints. Knowledge-based technology is an important factor in automation of configuration tasks found in mechanical design. In this paper, we describe a configuration technique that is well suited for configuring “decomposable” artifacts with reasonably well defined structure and constraints. This technique may be classified as a member of a general class of decompositional approaches to configuration. The domain knowledge is structured as a general model of the artifact, an and-or hierarchy of the artifact’s elements, features, and characteristics. The model includes constraints and local specialists which are attached to the elements of the and-or tree. Given the specific configuration requirements, the problem solving engine searches for a solution, a subtree, that satisfies the requirements and the applicable constraints. We describe an application of this approach that performs configuration and design of an automotive component.

Configuration Tree Solver: A Technology for Automated Design and Configuration

1990 ◽  
Author(s):  
Alexander Kott ◽  
Gerald Agin ◽  
Dave Fawcett
Keyword(s):  
Problem Solving ◽  
General Model ◽  
General Class ◽  
Domain Knowledge ◽  
Automated Design ◽  
Automotive Component ◽  
Business Operation ◽  
Knowledge Based ◽  
Specific Configuration

Abstract Configuration is a process of generating a definitive description of a product or an order that satisfies a set of specified requirements and known constraints. Knowledge-based technology is an enabling factor in automation of configuration tasks found in the business operation. In this paper, we describe a configuration technique that is well suited for configuring “decomposable” artifacts with reasonably well defined structure and constraints. This technique may be classified as a member of a general class of decompositional approaches to configuration. The domain knowledge is structured as a general model of the artifact, an and-or hierarchy of the artifact’s elements, features, and characteristics. The model includes constraints and local specialists which are attached to the elements of the and-or-tree. Given the specific configuration requirements, the problem solving engine searches for a solution, a subtree, that satisfies the requirements and the applicable constraints. We describe an application of this approach that performs configuration and design of an automotive component.

scholarly journals Mapping to Declarative Knowledge for Word Problem Solving

2018 ◽  
Vol 6 ◽  
pp. 159-172
Author(s):  
Subhro Roy ◽  
Dan Roth
Keyword(s):  
Problem Solving ◽  
Word Problem ◽  
Latent Variable ◽  
Domain Knowledge ◽  
Declarative Knowledge ◽  
Training Data ◽  
Mathematical Concepts ◽  
Word Problem Solving ◽  
Knowledge Based ◽  
Financial News

Math word problems form a natural abstraction to a range of quantitative reasoning problems, such as understanding financial news, sports results, and casualties of war. Solving such problems requires the understanding of several mathematical concepts such as dimensional analysis, subset relationships, etc. In this paper, we develop declarative rules which govern the translation of natural language description of these concepts to math expressions. We then present a framework for incorporating such declarative knowledge into word problem solving. Our method learns to map arithmetic word problem text to math expressions, by learning to select the relevant declarative knowledge for each operation of the solution expression. This provides a way to handle multiple concepts in the same problem while, at the same time, supporting interpretability of the answer expression. Our method models the mapping to declarative knowledge as a latent variable, thus removing the need for expensive annotations. Experimental evaluation suggests that our domain knowledge based solver outperforms all other systems, and that it generalizes better in the realistic case where the training data it is exposed to is biased in a different way than the test data.

Generic tasks as building blocks for knowledge-based systems: the diagnosis and routine design examples

1988 ◽  
Vol 3 (3) ◽  
pp. 183-210 ◽  
Author(s):  
B. Chandrasekaran
Keyword(s):  
Problem Solving ◽  
Domain Knowledge ◽  
Building Blocks ◽  
Knowledge Based Systems ◽  
Diagnostic Systems ◽  
Planning Systems ◽  
Knowledge Based ◽  
Generic Tasks ◽  
High Level ◽  
And Control

AbstractThe level of abstraction of much of the work in knowledge-based systems (the rule, frame, logic level) is too low to provide a rich enough vocabulary for knowledge and control. I provide an overview of a framework called the Generic Task approach that proposes that knowledge systems should be built out of building blocks, each of which is appropriate for a basic type of problem solving. Each generic task uses forms of knowledge and control strategies that are characteristic to it, and are in general conceptually closer to domain knowledge. This facilitates knowledge acquisition and can produce a more perspicuous explanation of problem solving. The relationship of the constructs at the generic task level to the rule-frame level is analogous to that between high-level programming languages and assembly languages in computer science. I describe a set of generic tasks that have been found particularly useful in constructing diagnostic, design and planning systems. In particular, I describe two tools, CSRL and DSPL, that are useful for building classification-based diagnostic systems and skeletal planning systems respectively, and a high level toolbox that is under construction called the Generic Task toolbox.

A model of the mechanical design process based on empirical data

1988 ◽  
Vol 2 (1) ◽  
pp. 33-52 ◽  
Author(s):  
David G. Ullman ◽  
Thomas G. Dietterich ◽  
Larry A. Stauffer
Keyword(s):  
Problem Solving ◽  
Detailed Analysis ◽  
Conceptual Design ◽  
Design Process ◽  
Empirical Data ◽  
Mechanical Design ◽  
Level Of Detail ◽  
Cad Tools ◽  
Design Alternatives ◽  
Key Features

This paper describes the task/episode accumulation model (TEA model) of non-routine mechanical design, which was developed after detailed analysis of the audio and video protocols of five mechanical designers. The model is able to explain the behavior of designers at a much finer level of detail than previous models. The key features of the model are (a) the design is constructed by incrementally refining and patching an initial conceptual design, (b) design alternatives are not considered outside the boundaries of design episodes (which are short stretches of problem solving aimed at specific goals), (c) the design process is controlled locally, primarily at the level of individual episodes. Among the implications of the model are the following: (a) CAD tools should be extended to represent the state of the design at more abstract levels, (b) CAD tools should help the designer manage constraints, and (c) CAD tools should be designed to give cognitive support to the designer.

Predicting readers’ domain knowledge based on eye-tracking measures

2018 ◽  
Vol 36 (6) ◽  
pp. 1027-1042 ◽  
Author(s):  
Quan Lu ◽  
Jiyue Zhang ◽  
Jing Chen ◽  
Ji Li
Keyword(s):  
Logistic Regression ◽  
Eye Tracking ◽  
Information Search ◽  
Domain Knowledge ◽  
Fixation Duration ◽  
Baseline Model ◽  
Content Type ◽  
Knowledge Based ◽  
Total Fixation ◽  
Negative Predictor

Purpose This paper aims to examine the effect of domain knowledge on eye-tracking measures and predict readers’ domain knowledge from these measures in a navigational table of contents (N-TOC) system. Design/methodology/approach A controlled experiment of three reading tasks was conducted in an N-TOC system for 24 postgraduates of Wuhan University. Data including fixation duration, fixation count and inter-scanning transitions were collected and calculated. Participants’ domain knowledge was measured by pre-experiment questionnaires. Logistic regression analysis was leveraged to build the prediction model and the model’s performance was evaluated based on baseline model. Findings The results showed that novices spent significantly more time in fixating on text area than experts, because of the difficulty of understanding the information of text area. Total fixation duration on text area (TFD_T) was a significantly negative predictor of domain knowledge. The prediction performance of logistic regression model using eye-tracking measures was better than baseline model, with the accuracy, precision and F(β = 1) scores to be 0.71, 0.86, 0.79. Originality/value Little research has been reported in literature on investigation of domain knowledge effect on eye-tracking measures during reading and prediction of domain knowledge based on eye-tracking measures. Most studies focus on multimedia learning. With respect to the prediction of domain knowledge, only some studies are found in the field of information search. This paper makes a good contribution to the literature on the effect of domain knowledge on eye-tracking measures during N-TOC reading and predicting domain knowledge.

Problem solving methods and knowledge systems: A personal journey to perceptual images as knowledge

2009 ◽  
Vol 23 (4) ◽  
pp. 331-338
Author(s):  
B. Chandrasekaran
Keyword(s):  
Problem Solving ◽  
Cognitive Architecture ◽  
Mental Image ◽  
Spatial Relations ◽  
Point Of View ◽  
Design Strategies ◽  
Symbolic Form ◽  
Knowledge Based ◽  
High Level ◽  
Use Of Knowledge

AbstractI was among those who proposed problem solving methods (PSMs) in the late 1970s and early 1980s as a knowledge-level description of strategies useful in building knowledge-based systems. This paper summarizes the evolution of my ideas in the last two decades. I start with a review of the original ideas. From an artificial intelligence (AI) point of view, it is not PSMs as such, which are essentially high-level design strategies for computation, that are interesting, but PSMs associated with tasks that have a relation to AI and cognition. They are also interesting with respect to cognitive architecture proposals such as Soar and ACT-R: PSMs are observed regularities in the use of knowledge that an exclusive focus on the architecture level might miss, the latter providing no vocabulary to talk about these regularities. PSMs in the original conception are closely connected to a specific view of knowledge: symbolic expressions represented in a repository and retrieved as needed. I join critics of this view, and maintain with them that most often knowledge is not retrieved from a base as much as constructed as needed. This criticism, however, raises the question of what is in memory that is not knowledge as traditionally conceived in AI, but can support theconstructionof knowledge in predicate–symbolic form. My recent proposal about cognition and multimodality offers a possible answer. In this view, much of memory consists of perceptual and kinesthetic images, which can be recalled during deliberation and from which internal perception can generate linguistic–symbolic knowledge. For example, from a mental image of a configuration of objects, numerous sentences can be constructed describing spatial relations between the objects. My work on diagrammatic reasoning is an implemented example of how this might work. These internal perceptions on imagistic representations are a new kind of PSM.

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