PPT-A Distributed Constraint Satisfaction Problem Approach to Virtual Device Composition

Eric Karmouch Amiya Nayak Paper Presentation by Michael Matarazzo mfm11vtedu A Distributed Constraint Satisfaction Problem Approach to Virtual Device Composition Eric Karmouch. CSD 15-780: Graduate Artificial Intelligence. Instructors: . Zico. . Kolter. and Zack Rubinstein. TA: Vittorio . Perera. 2. Constraint satisfaction problems. A . constraint satisfaction problem. (CSP): A set of . by a . set of . variables. {A,B,C,…}, a set . of domain . values. for these . variables, and a . set of . constraints. {R. 1. ,R. 2. ,R. 3. ,…} restricting . the allowable combinations of values for . Marek Perkowski. Projects for the new ECE 574 class. Project based class. Graduate class - . no prerequisities. C/C++ welcome, . but not mandatory. Verilog/VHDL welcome, . but not mandatory. .. FPGA. Approximate Algorithms. Alessandro Farinelli. Approximate Algorithms: outline. No guarantees. DSA-1, MGM-1 (exchange individual assignments). Max-Sum (exchange functions). Off-Line guarantees. K-optimality and extensions. Search when states are factored. Until now, we assumed states are black-boxes.. We will now assume that states are made up of “state-variables” and their “values”. Two interesting problem classes. Jared Cantwell. Review. Multicast. Causal and total ordering. Consistent Cuts. Synchronized clocks. Impossibility of consensus. Distributed file systems. Goal. Distributed programming is hard. What tools can make it easier?. Carl Waldspurger (SB SM . ’89, . PhD . ’95), VMware . R&D. Overview. Virtualization and VMs. Processor Virtualization. Memory Virtualization. I/O Virtualization. Types. of Virtualization. Process Virtualization. Optimization in Multi-Agent Systems. At the end of this talk, you will be able to:. Model decision making problems with DCOPs. Motivations for using DCOP . Modeling practical problems using DCOPs. Understand main exact techniques for DCOPs. exchange of education in Europe: the case of social security . education. María . Lucero. , PhD.. EFESE Project. When . we talk about labour markets and the social security system, we start by recognizing that, in . ). C. onstraint Propagation and Local Search. This lecture topic (two lectures). Chapter 6.1 – 6.4, except 6.3.3. Next lecture topic (two lectures). Chapter 7.1 – 7.5. (Please read lecture topic material before and after each lecture on that topic). Problems. . vs. . . Finite State Problems . Finite . State Problems (FSP). FSP can . be solved by searching in a space of . simple states. . . Finite states are . evaluated by domain-specific heuristics (rules) and tested to see whether they were goal states. . Problems. . vs. . . Finite State Problems . Finite . State Problems (FSP). FSP can . be solved by searching in a space of . simple states. . . Finite states are . evaluated by domain-specific heuristics (rules) and tested to see whether they were goal states. . Rhea . McCaslin. The GDS Network. Guarded Discrete Stochastic – neural network developed by Johnston and . Adorf. 2. Hubble Space Telescope. Scheduling Problem. PROBLEM: Between 10,000 – 30,000 astronomical observations per year . Rhea . McCaslin. The GDS Network. Guarded Discrete Stochastic – neural network developed by Johnston and . Adorf. 2. Hubble Space Telescope. Scheduling Problem. PROBLEM: Between 10,000 – 30,000 astronomical observations per year .