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               <mods:name>
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                     <mods:roleTerm type="text">author</mods:roleTerm>
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                  <mods:namePart>Furelos Blanco, Daniel</mods:namePart>
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                  <mods:dateIssued encoding="iso8601">2017-10-27T10:19:58Z2017-10-27T10:19:58Z2017-09</mods:dateIssued>
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               <mods:abstract>Tutor: Anders JonssonTreball fi de màster de: Master in Intelligent Interactive SystemsIn 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.</mods:abstract>
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               <mods:accessCondition type="useAndReproduction">Atribución-NoComercial-SinDerivadas 3.0 España http://creativecommons.org/licenses/by-nc-nd/3.0/es/ info:eu-repo/semantics/openAccess</mods:accessCondition>
               <mods:subject>
                  <mods:topic>Intel·ligència artificial</mods:topic>
               </mods:subject>
               <mods:subject>
                  <mods:topic>Sistemes multiagent</mods:topic>
               </mods:subject>
               <mods:subject>
                  <mods:topic>Classical planning</mods:topic>
               </mods:subject>
               <mods:subject>
                  <mods:topic>Concurrent planning</mods:topic>
               </mods:subject>
               <mods:subject>
                  <mods:topic>Multiagent planning</mods:topic>
               </mods:subject>
               <mods:subject>
                  <mods:topic>Temporal planning</mods:topic>
               </mods:subject>
               <mods:titleInfo>
                  <mods:title>Resolution of concurrent planning problems using classical planning</mods:title>
               </mods:titleInfo>
               <mods:genre>info:eu-repo/semantics/masterThesis</mods:genre>
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