Browsing by Author "Dr. Dennis R. Bahler, Committee Member"

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    Modeling and Enacting Business Processes via Commitment Protocols Among Agents
    (2005-12-08) Mallya, Ashok Ullal; Dr. Dennis R. Bahler, Committee Member; Dr. Munindar P. Singh, Committee Chair; Dr. Rada Y. Chirkova, Committee Member; Dr. James C. Lester, Committee Member
    Multiagent systems involve a rich variety of interactions among agents—situated computations that are autonomous in their behavior and heterogenous in structure. These interactions can be realized unambiguously if they are governed by published protocols, since agents diverse in their structure and behavior can interact as long as they respect the protocols. However, traditional protocol specifications are unduly rigid for application in open settings involving autonomous entities. They represent protocols simply as an ordering of steps and stifle the participants' autonomy due to a lack of flexibility during enactment. Commitments among agents, which are akin to contractual obligations among businesses, are a powerful abstraction for modeling flexible protocols. Commitment-based design enables a more faithful model of the openness of the business world. However, modeling business interactions requires a rich variety of interaction protocols that can capture the needs of different applications. Whereas general (business) protocols might most flexibly characterize the interactions of their participants, protocols often must be refined based on the environment in which they are to be deployed, so as to yield improvements along various properties such as performance and risk outlay, when applied to real-world tasks such as in e-business. We introduce a formal semantics and an operational characterization for commitmentbased protocols wherein traditional software engineering notions such as refinement and aggregation are extended to apply to protocols. We also develop a principled approach for the design of such protocols in addition to methodologies for modeling and handling exceptions in them. We demonstrate, with appropriate examples, the benefits of this approach over traditional ones when applied to business process modeling and enactment. Our chief contributions are - A theoretical basis for describing protocol refinement using subsumption hierarchies and an algebra for composing protocols using existing ones. - A methodology for modeling and handling exceptions in commitment protocols that incorporates the preferences of the protocol designer and policies of the participants and enables specification of exceptions independent of the protocol specification. - Two methodologies for designing commitment protocols, one by enhancing an existing agent-oriented software engineering methodology, and another by deriving protocols from agent conversations. Our work draws from and contributes to agent communication, business process modeling and enactment, service-oriented computing, and software engineering.
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    Optimization Algorithms for the Minimum-Cost Satisability Problem.
    (2004-10-07) Li, Xiao Yu; Dr. S. Purushothaman Iyer, Committee Member; Dr. Dennis R. Bahler, Committee Member; Dr. Matthias F. Stallmann, Committee Chair; Dr. Franc Brglez, Committee Co-Chair
    Given a Boolean satisability (Sat) problem whose variables have non-negative weights, the minimum-cost satisability (MinCostSat) problem finds a satisfying truth assignment that minimizes a weighted sum of the truth values of the variables. Many NP-optimization problems are either special cases of MinCostSat or can be transformed into MinCostSat efficiently. However, in the past, these problems have been largely considered in isolation. In this dissertation, we (1) classify existing MinCostSat problems, (2) study factors affecting the performance of MinCostSat solvers, (3) propose algorithms for MinCostSat problems, and (4) implement and validate the performance of state-of-the-art solvers for special cases of MinCostSat, including set and binate covering, Max-Sat, and group-partial Max-Sat. We categorize MinCostSat problems as either native or non-native. Non-native problems can only be transformed into MinCostSat by adding slack variables. These problems include the Max-Sat, partial Max-Sat, and group-partial Max-Sat problems which have applications ranging from course assignment to FPGA detailed routing. Native problems are various sub-cases of MinCostSat. We further divide these into two groups: covering problems and non-covering problems. The covering problems include the unate or set covering and the binate covering problems. They have applications in Operations Research (e.g., routing and scheduling) and logic synthesis (e.g., logic minimization and finite state machine minimization). In the covering problems, all or most clauses contain no complemented variables and a feasible solution is relatively easy to find. The non-covering problems contain clauses that are highly constrained, and sometimes only a small fraction of the variables are weighted. The non-covering problems have applications in minimum-size test pattern and minimum-length plans. We study two important performance factors, among others, in branch-and-bound MinCostSat solvers. They are the lower-bounding and upper-bounding strategies. For lower bounding, we incorporate two advanced techniques: linear programming relaxation and cutting planes. Both methods can provide much higher quality lower-bounds than previous methods based on maximum independent sets of rows. For upper bounding, we show that our local-search MinCostSat solver, when initialized and terminated properly, can find the best upper-bound quickly. Other techniques that contribute to the engineering of state-of-the-art solvers for applications of MinCostSat are also introduced. This work has led to the development of (1) a solver for covering problems that consistently outperforms previous leading solvers by as much as two orders of magnitude, (2) a logic minimizer that is able to solve three benchmark problems whose solution has eluded solvers for more than a decade, (3) a Max-Sat solver that challenges the dominance of linear programming solvers, particularly cplex, on Max-2-Sat benchmarks, and (4) a stochastic local-search solver for group-partial Max-Sat (with applications to FPGA routing) that finds known optima quickly and is able to find better than previously-known solutions on benchmarks whose optima remain unknown.