INTRODUCTION
	Lecture 1: Introduction [Thurs 9/07] Course information. What is Artificial Intelligence? A brief history of AI, its goals and achievements. Computer systems as intelligent agents. Types of environments, agents, and performance measures. Reflex agents, agents that keep track of the world, goal-based agents, and utility-based agents. Reading: AIMA Chptrs 1&2.
PROBLEM SOLVING USING SEARCH
	Lecture 2: Overview of Issues in Heuristic Search [Tues 9/12] Key concepts in heuristic search, state search paradigm and its complexities, local control/non-local control, satisficing and bounded-rationality, data, solution and control uncertainty, multi-level search, meta-level control, open vs. closed world assumption, resource bounds. Reading: Russel and Norvig, Chapter 3.
	Lecture 3: Blind search [Thurs 9/14] Algorithms for guiding search using heuristic information. The nature and origin of heuristics. Reading: Sections 4.1-4.4.
	Lecture 4: Heuristic search [Tues 9/19] Best-first search, A* and IDA*. Admissible evaluation functions. The effect of heuristic error. K-Best-First Search. Reading: Sections 4.1-4.4.
	Lecture 5: Time and space variations of A* [Thurs 9/21] IDA*, RBFS, SMA*, Memory-bounded heuristic search, RTA* Reading : Richard E. Korf, Real-Time Heuristic Search, Artificial Intellligence 42 (1990), pp 189-211.
	Lecture 6: Search complexity and applications [Tues 9/26] Anytime A*, Hierarchical A*, Other Examples of Hierarchical Problem Solving Readings: Eric A. Hansen, Shlomo Zilberstein, Victor A. Danilchenko, Anytime Heuristic Search: First Results, CS Technical Report, 97-50, UMASS Hierarchical A*: R.C. Holte, M.B. Perez, R.M.Zimmer, A.J. Macdonald, Hierarchical A*: Searching Abstraction Hierarchies Efficiently, {AAAI}/{IAAI}, Vol. 1, pp 530-535, 1996 Other Examples of Hierarchical Problem Solving: Craig A. Knoblock, Abstracting the Tower of Hanoi, In Proceedings of the Workshop on Automatic Generation of Approximations and Abstractions, pages 13--23, Boston, MA, 1990
	Lecture 7: Local Search [Thurs 9/28] Beam search, Hill Climbing, Genetic Algorithms, Simulated Annealing, Iterated Improvement. Stochastic Search.
	Lecture 8: CSPs: Heuristics for CSPs [Tues 10/03] Heuristic Repair for CSPs, Texture Measures, Solving CSPs using Systematic Search, Relationship of Problem structure to complexity. Reading: Section 4.4-4.5; Bart Selman, Hector Levesque and David Mitchell A New Method for Solving Hard Satisfiability Problems.. Proceedings AAAI-92; Steven Minton, Andy Philips, Mark D. Johnston and Philip Laird. Minimizing Conflicts: A Heuristic Repair Method for Constraint-Satisfaction and Scheduling Problems. Journal of Artificial Intelligence Research 1 (1993) 1-15.
	Lecture 9: CSPs, Interaction of Subproblems, Multi-Level Search [Thurs 10/05] Bactracking, K-consistency. Problem instance hardness, Necceisity of multi-level search, begin blackboard system discussion..
	Lecture 10: Blackboard Systems as an Architecture for Interpretation [Tues 10/10] Basic concepts of blackboard systems, separating control and domain problem solving, knowledge sources, multi-level search space. Reading: Erman, L.D., Hayes-Roth, F., Lesser, V.R., and Reddy, D.R. (1980). The HEARSAY-II Speech Understanding System: Integrating Knowledge to Resolve Uncertainty. Computing Surveys 12, (2), 213-253, 1980. Additional (optional) reading: Carver, N. and Lesser, V. The Evolution of Blackboard Control Architectures. Computer Science Technical Report 92-71, University of Massachusetts, Amherst. (This is a revised and extended version of paper with same title in Expert Systems with Applications: Special Issue on the Blackboard Paradigm and Its Applications.)
	Lecture 11: Sequential Decision Processes [Thurs 10/12] Continuation of Blackboard systems, Markov Decision Processes and Partially Observable Markov Decision Processes. Reading: Sections 17.1-17.4
	Lecture 12: Markov Decision Processes [Tues 10/17] Continuation of Markov Decision Processes.
	Lecture 13: POMDPs and Intro to HMMs[Thurs 10/19] More uses of Bayes' rule. Introduction to graphical models, specifically (Bayes') belief networks, d-seperation, noisy-OR Reading: Chapter 14.
	Lecture 14: Hidden Markov Models [Tues 10/24] Continuation of Hidden Markov Models. Reading: A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition, Sections I-III.
MIDTERM [Thurs 10/26]
midterm 1996 midterm 1998 midterm 2000 solutions 2000 midterm 2002 solutions 2006
REASONING UNDER UNCERTAINTY
	Lecture 15: Uncertainty [Tues 10/31] Sources of uncertainty, representing uncertainty, Bayesian reasoning. Bayes' Rule and its uncertainty. Reading: Chapter 13.
	Lecture 16: Probabilistic reasoning with belief networks[Thurs 11/02] More uses of Bayes' rule. Introduction to graphical models, specifically (Bayes') belief networks, d-seperation, noisy-OR Reading: Chapter 14.
	Lecture 17: Probabilistic reasoning with belief networks 2 [Tues 11/07] Network construction, Inference in BNs automated belief propagation in polytrees, exact inference in tree-structured networks, inference in multiply connected BNs.
	Lecture 18: Approximate inference for BNs Alternative Aproaches to uncertainty.[Tues 11/14] Inference in multiply connected belief networks. Clustering methods, cutset conditioning, and stochastic simulation. Alternative approaches to uncertain reasoning. Reading: Sections 14.4-14.7.
	Lecture 19: Decision Theory [Thurs 11/16] Making optimal decisions by maximizing utility. The axioms of decision theory. Utility scales and utility assessment. . Reading: Chapter 16
LEARNING
	Lecture 20: Decision Networks [Tues 11/21] Decision tree methods. The semantics of decision networks. Evaluating decision networks. Reading: Section 18.3
	No Class Thankgiving Holiday [Thurs 11/23]
	Class Canceled (Make up on Friday 12/1) [Tues 11/28]
	Lecture 21: Learning from observations [Thurs 11/30] How to get intelligent systems to learn from their experience. Inducing rules from data. Learning decision trees. Learning general logical descriptions. Reading: Sections 18.1,2,4,5.
	Lecture 22: Make up Class (same time and place) [Fri 12/01] Decision tree issues. Current-best-hypothesis search. Version Space. Neural network introduction.
	Lecture 23: Neural networks [Tues 12/05] Network structure, perceptrons, Hopefield networks, associative memory, multi-layer feed-forward networks, applications. Reading: Sections 19.1-19.5, 20.8.
	Lecture 24: Reinforcement Learning [Thurs 12/07] Exploration versus exploitation, Q-Learning, degrees of abstraction, Reading: Sections 20.1-20.6.
	Lecture 25: Analytical Learning and Planning [Thurs 12/12] Analytical learning (explanation-based learning), an overview of planning
FINAL EXAM [Mon 12/18 8:00am LGRT 321]
final 1998 final 2000 final 2002 final 2004