منابع مشابه
Learning from planner performance
a r t i c l e i n f o a b s t r a c t The planning community has amassed a large body of publicly available problems in a standardized input language and planners that accept the language. We seized this remarkable opportunity to collect data about how some of these planners perform on the benchmark problems. We analyzed the resulting data to learn about the state of the art in Classical planni...
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To date, no one planner has demonstrated clearly superior performance. Although researchers have hypothesized that this should be the case, no one has performed a large study to test its limits. In this research, we tested performance of a set of planners to determine which is best on what types of problems. The study included six planners and over 200 problems. We found that performance, as me...
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Hoffmann’s topological analysis of the h and h F state spaces explained why Fast Forward dominated the performance on early planning domains (Hoffmann 2004). His taxonomy segmented domains according to the presence/size of local minima and dead-end class. Surprisingly, the taxonomy has not been used to explain the performance of other planners that use similar heuristics. In this paper, we exte...
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A promising approach to constructing more effective computer tutors is implementing tutorial strategies that extend over multiple turns. This means that computer tutors must deal with (1) failure, (2) interruptions, (3) the need to revise their tactics, and (4) basic dialogue phenomena such as acknowledgment. To deal with these issues, we need to combine ITS technology with advances from roboti...
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ژورنال
عنوان ژورنال: Artificial Intelligence
سال: 2009
ISSN: 0004-3702
DOI: 10.1016/j.artint.2008.11.009