ReAct!: an interactive educational tool for AI planning for robotics

Doğmuş, Zeynep and Erdem, Esra and Patoğlu, Volkan (2015) ReAct!: an interactive educational tool for AI planning for robotics. IEEE Transactions on Education, 58 (1). pp. 15-24. ISSN 0018-9359 (Print) 1557-9638 (Online)

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Official URL: http://dx.doi.org/10.1109/TE.2014.2318678


This paper presents ReAct!, an interactive educational tool for artificial intelligence (AI) planning for robotics. ReAct! enables students to describe robots' actions and change in dynamic domains without first having to know about the syntactic and semantic details of the underlying formalism, and to solve planning problems using state-of-the-art reasoners without having to know about their input/output language or usage. In particular, ReAct! can be used to represent sophisticated dynamic domains that feature concurrency, indirect effects of actions, and state/transition constraints. ReAct! also allows the embedding of externally defined calculations (e.g., checking for collision-free continuous trajectories) into domain representations of hybrid domains that require a tight integration of (discrete) high-level reasoning with (continuous) geometric reasoning. ReAct! also allows students to solve planning problems that involve complex temporal goals. This broad applicability, and the intelligent interactive user interface, mean that students can work on interesting and challenging domains, ranging from service robotics to cognitive factories, leading to hands-on robotic applications. The efficacy of ReAct! was evaluated from three different points of view: 1) the course outcomes that demonstrate its utility in achieving the learning objectives of a research-oriented cognitive robotics course; 2) the user friendliness and usefulness of ReAct! for students, as evaluated by quantitative student surveys; and 3) instructors' experience of teaching the course either with or without ReAct!.

Item Type:Article
Uncontrolled Keywords:Artificial intelligence (AI) planning, automated reasoning, hybrid planning, knowledge representation, robotics
ID Code:26134
Deposited By:Volkan Patoğlu
Deposited On:13 Dec 2014 13:49
Last Modified:12 Dec 2015 21:50

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