<oai_dc:dc xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
  <dc:creator>Gjoreski, Martin</dc:creator>
  <dc:creator>Kuzmanovski, Vladimir</dc:creator>
  <dc:creator>Bohanec, Marko</dc:creator>
  <dc:date>2022</dc:date>
  <dc:description xmlns:ns0="xml" ns0:lang="en">Multi-attribute decision analysis is an approach to decision support in which decision alternatives are evaluated by multi-criteria models. An advanced feature of decision support models is the possibility to search for new alternatives that satisfy certain conditions. This task is important for practical decision support; however, the related work on generating alternatives for qualitative multi-attribute decision models is quite scarce. In this paper, we introduce Bayesian Alternative Generator for Decision Support Models (BAG-DSM), a method to address the problem of generating alternatives. More specifically, given a multi-attribute hierarchical model and an alternative representing the initial state, the goal is to generate alternatives that demand the least change in the provided alternative to obtain a desirable outcome. The brute force approach has exponential time complexity and has prohibitively long execution times, even for moderately sized models. BAG-DSM avoids these problems by using a Bayesian optimization approach adapted to qualitative DEX models. BAG-DSM was extensively evaluated and compared to a baseline method on 43 different DEX decision models with varying complexity, e.g., different depth and attribute importance. The comparison was performed with respect to: the time to obtain the first appropriate alternative, the number of generated alternatives, and the number of attribute changes required to reach the generated alternatives. BAG-DSM outperforms the baseline in all of the experiments by a large margin. Additionally, the evaluation confirms BAG-DSM’s suitability for the task, i.e., on average, it generates at least one appropriate alternative within two seconds. The relation between the depth of the multi-attribute hierarchical models—a parameter that increases the search space exponentially—and the time to obtaining the first appropriate alternative was linear and not exponential, by which BAG-DSM’s scalability is empirically confirmed.</dc:description>
  <dc:format>application/pdf</dc:format>
  <dc:identifier>https://n2t.net/ark:/12658/srd1322963</dc:identifier>
  <dc:identifier>https://susi.usi.ch/global/documents/322963</dc:identifier>
  <dc:identifier>https://susi.usi.ch/documents/322963/files/Gjoreski_2022_MDPI_algorithms.pdf</dc:identifier>
  <dc:language>eng</dc:language>
  <dc:relation>info:eu-repo/semantics/altIdentifier/doi/10.3390/a15060197</dc:relation>
  <dc:relation>info:eu-repo/semantics/altIdentifier/ark/12658/srd1322963</dc:relation>
  <dc:rights>info:eu-repo/semantics/openAccess</dc:rights>
  <dc:rights>CC BY</dc:rights>
  <dc:source>Algorithms. - 2022, vol. 15, no. 6, p. 197</dc:source>
  <dc:subject xmlns:ns1="xml" ns1:lang="en">multi-attribute models</dc:subject>
  <dc:subject xmlns:ns2="xml" ns2:lang="en">method DEX</dc:subject>
  <dc:subject xmlns:ns3="xml" ns3:lang="en">alternatives</dc:subject>
  <dc:subject xmlns:ns4="xml" ns4:lang="en">decision support</dc:subject>
  <dc:subject xmlns:ns5="xml" ns5:lang="en">Bayesian optimization</dc:subject>
  <dc:subject>info:eu-repo/classification/udc/51</dc:subject>
  <dc:title xmlns:ns6="xml" ns6:lang="en">BAG-DSM : a method for generating alternatives for hierarchical multi-attribute decision models using bayesian optimization</dc:title>
  <dc:type>http://purl.org/coar/resource_type/c_6501</dc:type>
</oai_dc:dc>
