2019
DOI: 10.1007/978-3-030-14805-8_10
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Building a Generic Feedback System for Rule-Based Problems

Abstract: We present a generic framework that provides hints on how to achieve a goal to users of software supporting rule-based problem solving from di↵erent domains. Our approach consists of two parts. First, we present a DSL that relates and unifies di↵erent rule-based problems. Second, we use generic search algorithms to solve various kinds of problems. This solution can then be used to calculate a hint for the user. We present three rule-based problem frameworks to illustrate our approach: the Ideas framework, Puzz… Show more

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Cited by 5 publications
(5 citation statements)
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“…Naus and Jeuring [19] have looked at building a generic feedback system for rule-based problems. A workflow system typically is rule based, as outlined in their work.…”
Section: Discussionmentioning
confidence: 99%
“…Naus and Jeuring [19] have looked at building a generic feedback system for rule-based problems. A workflow system typically is rule based, as outlined in their work.…”
Section: Discussionmentioning
confidence: 99%
“…In previous work, we have attempted to provide end users with next step hints by viewing workflows as rule based problems [15]. By abstracting over workflows, reasoning about them becomes simpler.…”
Section: Related Workmentioning
confidence: 99%
“…To overcome these difficulties, we propose to integrate a next step hint system into workflow software. By combining previous research on symbolic execution for Task-Oriented Programming [16] and end-user feedback systems for rule based problems [15], we develop a next step hint end-user feedback system for the Task-Oriented Programming language TopHat ( TOP) [20]. Our solution, which we call Assistive TOP, generates next step hints from existing code, and does not require extra work by the programmer.…”
Section: Introductionmentioning
confidence: 99%
“…Designing a real-time controller for robots with high-speed motion is a challenging problem. Conventional approaches of designing a rule-based feedback controller [1]- [3] have shortcomings in covering all the exception cases. Recent approaches include a method that can autonomously cover all the exception cases, such as training the controller via deep reinforcement learning (RL) methods [4]- [8].…”
Section: Introductionmentioning
confidence: 99%