Case-Based Plan Recognition Using Action Sequence Graphs
We present SET-PR, a novel case-based plan recognition algorithm that is tolerant to missing and misclassified actions in its input action sequences. SET-PR uses a novel representation called action sequence graphs to represent stored plans in its plan library and a similarity metric that uses a com...
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| creator | Vattam, Swaroop S Aha, David W Floyd, Michael |
| description | We present SET-PR, a novel case-based plan recognition algorithm that is tolerant to missing and misclassified actions in its input action sequences. SET-PR uses a novel representation called action sequence graphs to represent stored plans in its plan library and a similarity metric that uses a combination of graph degree sequences and object similarity to retrieve relevant plans from its library. We evaluated SET-PR by measuring plan recognition convergence and precision with increasing levels of missing and misclassified actions in its input. In our experiments, SET-PR tolerated 20%-30% of input errors without compromising plan recognition performance.
Published in Proceedings of the 22nd International Conference on Case-Based Reasoning, Held 29 Sep-1 Oct 2014, in Cork, Ireland, p495-510. |
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Published in Proceedings of the 22nd International Conference on Case-Based Reasoning, Held 29 Sep-1 Oct 2014, in Cork, Ireland, p495-510.</description><subject>ALGORITHMS</subject><subject>ARTIFICIAL INTELLIGENCE</subject><subject>CASE-BASED REASONING</subject><subject>CONVERGENCE</subject><subject>ERROR TOLERANCE</subject><subject>ERRORS</subject><subject>GRAPH MATCHING</subject><subject>GRAPHS</subject><subject>Numerical Mathematics</subject><subject>PLAN RECOGNITION</subject><subject>PLANNING</subject><subject>PRECISION</subject><subject>REASONING</subject><subject>RECOGNITION</subject><subject>SEQUENCES</subject><subject>SYMPOSIA</subject><subject>TOLERANCE</subject><fulltext>true</fulltext><rsrctype>report</rsrctype><creationdate>2014</creationdate><recordtype>report</recordtype><sourceid>1RU</sourceid><recordid>eNrjZLBwTixO1XUCEikKATmJeQpBqcn56XmZJZn5eQqhxZl56QqOyWBOcGphaWpecqqCe1FiQUYxDwNrWmJOcSovlOZmkHFzDXH20E0pyUyOLy7JzEstiXd0cTQzNDM1sDQmIA0A3lMqpw</recordid><startdate>201410</startdate><enddate>201410</enddate><creator>Vattam, Swaroop S</creator><creator>Aha, David W</creator><creator>Floyd, Michael</creator><scope>1RU</scope><scope>BHM</scope></search><sort><creationdate>201410</creationdate><title>Case-Based Plan Recognition Using Action Sequence Graphs</title><author>Vattam, Swaroop S ; Aha, David W ; Floyd, Michael</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-dtic_stinet_ADA6165093</frbrgroupid><rsrctype>reports</rsrctype><prefilter>reports</prefilter><language>eng</language><creationdate>2014</creationdate><topic>ALGORITHMS</topic><topic>ARTIFICIAL INTELLIGENCE</topic><topic>CASE-BASED REASONING</topic><topic>CONVERGENCE</topic><topic>ERROR TOLERANCE</topic><topic>ERRORS</topic><topic>GRAPH MATCHING</topic><topic>GRAPHS</topic><topic>Numerical Mathematics</topic><topic>PLAN RECOGNITION</topic><topic>PLANNING</topic><topic>PRECISION</topic><topic>REASONING</topic><topic>RECOGNITION</topic><topic>SEQUENCES</topic><topic>SYMPOSIA</topic><topic>TOLERANCE</topic><toplevel>online_resources</toplevel><creatorcontrib>Vattam, Swaroop S</creatorcontrib><creatorcontrib>Aha, David W</creatorcontrib><creatorcontrib>Floyd, Michael</creatorcontrib><creatorcontrib>NAVAL RESEARCH LAB WASHINGTON DC NAVY CENTER FOR APPLIED RESEARCH IN ARTIFICIAL INTELLIGENCE</creatorcontrib><collection>DTIC Technical Reports</collection><collection>DTIC STINET</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Vattam, Swaroop S</au><au>Aha, David W</au><au>Floyd, Michael</au><aucorp>NAVAL RESEARCH LAB WASHINGTON DC NAVY CENTER FOR APPLIED RESEARCH IN ARTIFICIAL INTELLIGENCE</aucorp><format>book</format><genre>unknown</genre><ristype>RPRT</ristype><btitle>Case-Based Plan Recognition Using Action Sequence Graphs</btitle><date>2014-10</date><risdate>2014</risdate><abstract>We present SET-PR, a novel case-based plan recognition algorithm that is tolerant to missing and misclassified actions in its input action sequences. SET-PR uses a novel representation called action sequence graphs to represent stored plans in its plan library and a similarity metric that uses a combination of graph degree sequences and object similarity to retrieve relevant plans from its library. We evaluated SET-PR by measuring plan recognition convergence and precision with increasing levels of missing and misclassified actions in its input. In our experiments, SET-PR tolerated 20%-30% of input errors without compromising plan recognition performance.
Published in Proceedings of the 22nd International Conference on Case-Based Reasoning, Held 29 Sep-1 Oct 2014, in Cork, Ireland, p495-510.</abstract><oa>free_for_read</oa></addata></record> |
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| subjects | ALGORITHMS ARTIFICIAL INTELLIGENCE CASE-BASED REASONING CONVERGENCE ERROR TOLERANCE ERRORS GRAPH MATCHING GRAPHS Numerical Mathematics PLAN RECOGNITION PLANNING PRECISION REASONING RECOGNITION SEQUENCES SYMPOSIA TOLERANCE |
| title | Case-Based Plan Recognition Using Action Sequence Graphs |
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