Many real world problems are naturally distributed, whether they are spatially, cognitively, or otherwise. Distributed problems naturally lend themselves to solutions using multi-agent paradigms. Distributed Constraint Satisfaction Problems (DisCSPs) are a class of such distributed problems. In DisCSPs, variables and constraints are distributed between agents. Most distributed algorithms, although exponential in the worst-case, can have a good performance in the average case. The main purpose of this research is to statistically assess difference between the empirical performances of major state of the art DisCSP algorithms including Multi-Sectioned Constraint Network (MSCN) based algorithms, that have never been empirically compared against other DisCSP algorithms. In this thesis, we select a set of state of the art DisCSP algorithms and compare them on randomly generated instances of binary DisCSPs with a wide range of characteristics. Distributed algorithms ADOPT, DSA, DPOP, and MSCN based algorithms were selected based on a set of high level criteria. We explore how these algorithms relatively compare with each other on a range of DisCSPs with different parameters. Their performances are evaluated according to computation time (in the form of non-concurrent computational steps or NCCCs) and communication load (in the form of number of messages as well as volume of messages). Statistical parametric tests are used to aid interpretation of the performance results. In addition, this thesis discusses privacy issues associated with these DisCSP algorithms.
| Identifer | oai:union.ndltd.org:LACETR/oai:collectionscanada.gc.ca:OGU.10214/3038 |
| Date | 20 September 2011 |
| Creators | Mohamed, Younis |
| Contributors | Xiang, Yang |
| Source Sets | Library and Archives Canada ETDs Repository / Centre d'archives des thèses électroniques de Bibliothèque et Archives Canada |
| Language | English |
| Detected Language | English |
| Type | Thesis |
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