PPT-Constraint Satisfaction and Schemata

Psych 205 Goodness of Network States and their Probabilities Goodness of a network state How networks maximize goodness The Hopfield network and Rumelharts continuous version Stochastic networks The Boltzmann Machine and the relationship between goodness and . some pattern of mines in the blank squares that give rise to the numbers seen." Obviously,instances of the minesweeper problem have an answer of yes or no, as most problems studiedin computational co Introduction and Backtracking Search. This lecture:. CSP Introduction and Backtracking Search. Chapter 6.1 – 6.4, except 6.3.3. Next lecture:. CSP Constraint Propagation & Local Search. Chapter 6.1 – 6.4, except 6.3.3. 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 . Consumer. Behavior. Chapter 3. Discussion Topics. The concept of consumer utility (satisfaction). Evaluation of alternative consumption bundles using . indifference. curves. What is the role of your budget constraint in determining what you purchase?. 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. Psych 205. Goodness of Network States and their Probabilities. Goodness of a network state. How networks maximize goodness. The Hopfield network and . Rumelhart’s. continuous version. Stochastic networks: The Boltzmann Machine, and the relationship between goodness and . September 5,2014. How the brain works:. Each time we have experiences (stimuli) these are stored in memory (schemata). No one knows how the brain stores the experiences (stimuli, information). One explanation is that the brain stores information in “sectors” like a filing cabinet.. Genetic Algorithms. Sources. Material in this lecture comes from, “Handbook of Natural Computing,” Editors . Grzegorz. Rosenberg, Thomas Back and . Joost. N. . Kok. , Springer 2014. . In set S. Introduction and Backtracking 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. . Eric . Karmouch. , . Amiya. . Nayak. Paper Presentation by Michael . Matarazzo. (mfm11@vt.edu). A Distributed Constraint Satisfaction Problem Approach to Virtual Device Composition. Eric . Karmouch. 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. . R&N 6.1-6.4 (except 6.3.3). What is a . CSP?. Backtracking . search for CSPs. Choose a variable, then choose an order for values. Minimum Remaining Values (MRV), Degree Heuristic (DH), Least Constraining Value (LCV).