Abstract
Most approaches to plan recognition are based on manually constructed rules, where the knowledge base is represented as a plan library for recognising plans. For non-trivial domains, such plan libraries have complex structures representing possible agent behaviour to achieve a plan. Existing plan recognition approaches are seldom tested at their limits, and, though they use conceptually similar plan library representations, they rarely use the exact same domain in order to directly compare their performance, leading to the need for a principled approach to evaluating them. Thus, we develop a mechanism to automatically generate arbitrarily complex plan libraries which can be directed through a number of parameters, in order to create plan libraries representing different domains and so allowing systematic experimentation and comparison among the several plan recognition algorithms. We validate our mechanism by carrying out an experiment to evaluate the performance of a known plan recognition algorithm.
Original language | English |
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Title of host publication | 2016 5th Brazilian Conference on Intelligent Systems (BRACIS), |
Publisher | IEEE Explore |
Pages | 205-210 |
Number of pages | 6 |
DOIs | |
Publication status | Published - 2016 |
Event | 5th Brazilian Conference on Intelligent Systems (BRACIS) - Recife, Brazil , Brazil Duration: 9 Oct 2016 → 12 Oct 2016 |
Conference
Conference | 5th Brazilian Conference on Intelligent Systems (BRACIS) |
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Country/Territory | Brazil |
Period | 9/10/16 → 12/10/16 |
Bibliographical note
ACKNOWLEDGEMENTThis paper was part of the research project entitled “Semantic and Multi-Agent Technologies for Group Interaction”, sponsored by Samsung Eletrônica da Amazônia Ltda. under the terms of Brazilian federal law No. 8.248/91. This research was also supported by CAPES and CNPq. Felipe Meneguzzi thanks CNPq for the support within process numbers 306864/2013-4 under the PQ fellowship and 482156/2013-9 under the Universal project programs.