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   <dc:title>Resolution of concurrent planning problems using classical planning</dc:title>
   <dc:creator>Furelos Blanco, Daniel</dc:creator>
   <dc:subject>Intel·ligència artificial</dc:subject>
   <dc:subject>Sistemes multiagent</dc:subject>
   <dc:subject>Classical planning</dc:subject>
   <dc:subject>Concurrent planning</dc:subject>
   <dc:subject>Multiagent planning</dc:subject>
   <dc:subject>Temporal planning</dc:subject>
   <dcterms:abstract>Tutor: Anders Jonsson</dcterms:abstract>
   <dcterms:abstract>Treball fi de màster de: Master in Intelligent Interactive Systems</dcterms:abstract>
   <dcterms:abstract>In this work, we present new approaches for solving multiagent planning and temporal&#xd;
planning problems. These planning forms are two types of concurrent planning,&#xd;
where actions occur in parallel. The methods we propose rely on a compilation to&#xd;
classical planning problems that can be solved using an off-the-shelf classical planner.&#xd;
Then, the solutions can be converted back into multiagent or temporal solutions.&#xd;
Our compilation for multiagent planning is able to generate concurrent actions that&#xd;
satisfy a set of concurrency constraints. Furthermore, it avoids the exponential&#xd;
blowup associated with concurrent actions, a problem that many multiagent planners&#xd;
are facing nowadays. Incorporating similar ideas in temporal planning enables&#xd;
us to generate temporal plans with simultaneous events, which most state-of-the-art&#xd;
temporal planners cannot do.&#xd;
In experiments, we compare our approaches to other approaches. We show that the&#xd;
methods using transformations to classical planning are able to get better results&#xd;
than state-of-the-art approaches for complex problems. In contrast, we also highlight&#xd;
some of the drawbacks that this kind of methods have for both multiagent and&#xd;
temporal planning.&#xd;
We also illustrate how these methods can be applied to real world domains like the&#xd;
smart mobility domain. In this domain, a group of vehicles and passengers must&#xd;
self-adapt in order to reach their target positions. The adaptation process consists&#xd;
in running a concurrent planning algorithm. The behavior of the approach is then&#xd;
evaluated.</dcterms:abstract>
   <dcterms:issued>2017-10-27T10:19:58Z</dcterms:issued>
   <dcterms:issued>2017-10-27T10:19:58Z</dcterms:issued>
   <dcterms:issued>2017-09</dcterms:issued>
   <dc:type>info:eu-repo/semantics/masterThesis</dc:type>
   <dc:rights>Atribución-NoComercial-SinDerivadas 3.0 España</dc:rights>
   <dc:rights>http://creativecommons.org/licenses/by-nc-nd/3.0/es/</dc:rights>
   <dc:rights>info:eu-repo/semantics/openAccess</dc:rights>
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