Traditional artificial intelligence techniques do not perform well in applications such as real-time strategy games because of the extensive search spaces which need to be explored. In addition, this exploration must be carried out on-line during performance time; it cannot be precomputed. We have developed on-line case-based planning techniques that are effective in such domains. In this paper, we extend our earlier work using ideas from traditional planning to inform the real-time adaptation of plans. In our framework, when a plan is retrieved, a plan dependency graph is inferred to capture the relations between actions in the plan. The plan is then adapted in real-time using its plan dependency graph. This allows the system to create and adapt plans in an efficient and effective manner while performing the task. The approach is evaluated using WARGUS, a well-known real-time strategy game.
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On-Line Case-Based Plan Adaptation for Real-Time Strategy Games
by Neha Sugandh, Santi Ontañón, Ashwin Ram
23rd AAAI Conference on Artificial Intelligence (AAAI-08), Chicago, IL, July 2008
In this paper we investigate how to automatically determine the subjectivity orientation of questions posted by real users in community question answering (CQA) portals. Subjective questions seek answers containing private states, such as personal opinion and experience. In contrast, objective questions request objective, verifiable information, often with support from reliable sources. Knowing the question orientation would be helpful not only for evaluating answers provided by users, but also for guiding the CQA engine to process questions more intelligently. Our experiments on Yahoo! Answers data show that our method exhibits promising performance.
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Subjectivity Analysis for Questions in QA Communities
by Baoli Li, Yandong Liu, Ashwin Ram, Ernie Garcia, Eugene Agichtein
31st Annual International ACM SIGIR Conference (ACM-SIGIR-08), Singapore, July 2008