Principles of Artificial Intelligence: Study Guide The material to be covered each week and the assigned readings (along with online lecture notes, if available) are included on this page. The study guide (including slides, notes, readings) will be updated each week. The assigned readings are divided into required and recommended readings and notes from recitations (if available). You will be responsible for the material covered in the lectures and the assigned required readings. You are strongly encouraged to explore the recommended readings.

Week 1 (starting August 23, 2009) Overview of the course; Overview of artificial intelligence: What is intelligence? What is artificial intelligence (AI)? History of AI; Working hypothesis of AI. Introduction to intelligent agents. Intelligent agents defined. Taxonomy of agents.

Simple reflex agents (memoryless agents); agents with limited memory; rational agents; agents with goals; utility-driven Agents. Required Readings -- Artificial Intelligence •. • Vasant Honavar.

• • (Seminal paper by Alan Turing). • Chapters 1 and 2 from, Russell and Norvig. • by Jim Hendler. Feigenbaum, Journal of the ACM, Vol. •, Ray Kurzweil, Kurzweilai.net • page from the American Association for Artificial Intelligence. Ad Art Partai Golkar Pdf Download.

• page from the American Association for Artificial Intelligence. • page from the American Association for Artificial Intelligence.

Artificial intelligence. Artificial intelligence (AI), is a product of the com- puter age. Although people have fantasized about making intelligent creations since the dawn of. Search strategy. Mathematical tools (for example, the branch-and-bound alogorithim and various types of mathematical programming such as linear pro.

• page from the American Association for Artificial Intelligence. Required Readings -- Programming You may skip most of these readings if you have prior programming experience in Java. • Getting Started with Java • by Marv Solomon, University of Wisconsin-Madison. • (Skim through) from Sun Microsystems. • (Skim through) Chapters 1-12 from by B.

Week 2, Starting Aug 30, 2009 Goal-Based Agents. Problem-solving as state space search. Formulation of state-space search problems. Representing states and actions. Basic search algorithms and their properties: completeness, optimality, space and time complexity.

Breadth-first search, depth-first search, backtracking search, depth-limited and interative deepening search. Heuristic search. Finding optimal solutions. Best first search. A* Search: Adding Heuristics to Branch and Bound Search. Completeness, Admissibility, and Optimality of the A* algorithm.

Design of admissible heuristic functions. Comparison of heuristic functions ('informedness' of heuristics). Required readings • Chapter 3 and Chapter 4 (sections 4.1, 4.2, 4.3) from, Russell and Norvig. • Lecture Notes:, by Vasant Honavar. • (basic search), Vasant Honavar • (informed search), Vasant Honavar • Lecture Notes:, by Vasant Honavar Recommended readings • Zhang, W.

Artificial Intelligence, Vol. (2000)., Proceedings of AAAI, 2000. • Jensen, R.M., Bryant, R.E., and Veloso, M. (2002), In: Proceedings of AAAI, 2002.

And Hansen, E.A. (2003)., In: Proceedings of the IEEE Conference on Tools with Artificial Intelligence • Edelkamp, S., Jabbar, S., and Lluch-Lafuente, A.

(2005) In: Proceedings of the AAAI, 2005. And Perez de la Cruz J.L. (2003), European Journal of Operational Research, Volume 150, Number 2, 16 October 2003, pp. 253-280(28) • Furcy, D.A. (2004), Ph.D. Thesis, College of Computing, Georgia Tech. • Korf, R., Zhang, W., Thayer, I.

And Heath, H. (2005)., Journal of the ACM, Vol. (1998)., Proceedings of AAAI, 1998, pp. Additional Information • G. Polya, How to Solve It, Princeton University Press, 1957.

Heuristics: Intelligent Search Strategies for Computer Problem Solving. • page from the American Association for Artificial Intelligence. Week 3, Starting September 7, 2009 Problem Solving through Problem Reduction. Searching AND-OR graphs. A*-like admissible algorithm for searching AND-OR graphs. Problem solving as Constraint Satisfaction. Properties of constraint satisfaction problems.

Examples of constraint satisfaction problems. Iterative instantiation method for solving CSPs. Scene interpretation as constraint propagation (Waltz's line labeling algorithm). Node consistency, arc consistency, and related algorithms. Required readings • Section 4.3 and Chapter 5 from, Russell and Norvig. • (problem reduction representation), Vasant Honavar • Lecture Notes:, by Vasant Honavar.

• Chapter 5 from, Russell and Norvig. •, Vasant Honavar • Lecture Notes:, by Vasant Honavar •, by Roman Bartak • Allen, J., Communications of the ACM, Vol. Recommended readings •, by Edward Tsang.

•, by Roman Bartak • Chapters 1 and 2 from, by Edward Tsang. (chapters available for download). •, Vipin Kumar, 1992. © 1999-2007, Vasant Honavar, Department of Computer Science, Iowa State University. All rights reserved.

Contents • • • • • • Languages [ ] • (meaning 'Artificial Intelligence Markup Language') is an dialect for use with -type. • was the first language developed for artificial intelligence. It includes features intended to support programs that could perform general problem solving, such as lists, associations, schemas (frames), dynamic memory allocation, data types, recursion, associative retrieval, functions as arguments, generators (streams), and cooperative multitasking. • is a practical mathematical notation for computer programs based on. Are one of the Lisp language's major, and Lisp is itself made up of lists. As a result, Lisp programs can manipulate source code as a data structure, giving rise to the systems that allow programmers to create new syntax or even new embedded in Lisp.

There are many dialects of Lisp in use today, among which are,, and. • has been used extensively for simulations, neural networks, machine learning and genetic algorithms.

It implements the purest and most elegant form of object-oriented programming using message passing. • is a language where programs are expressed in terms of relations, and execution occurs by running queries over these relations. Prolog is particularly useful for symbolic reasoning, database and language parsing applications. Prolog is widely used in AI today. • is a language for expressing. It expresses an initial state, the goal states, and a set of actions.

For each action preconditions (what must be established before the action is performed) and postconditions (what is established after the action is performed) are specified. • is a hybrid between procedural and logical languages.

It gives a procedural interpretation to logical sentences where implications are interpreted with pattern-directed inference. • is a, with many of the features of an. It is the core language of the developed originally by the, and recently in the at the which hosts, It is often used to introduce symbolic programming techniques to programmers of more conventional languages like, who find POP syntax more familiar than that of.

One of POP-11's features is that it supports. • is widely used for artificial intelligence, with packages for a number of applications including General AI,, and. • is also a very good programming language for AI.

Lazy evaluation and the list and LogicT monads make it easy to express non-deterministic algorithms, which is often the case. Infinite data structures are great for search trees. Nik Color Efex Pro 3.0 For Capture Nx 2 Serial. The language's features enable a compositional way of expressing the algorithms. The only drawback is that working with graphs is a bit harder at first because of purity.

• includes a wide range of integrated machine learning capabilities, from highly automated functions like Predict and Classify to functions based on specific methods and diagnostics. The functions work on many types of data, including numerical, categorical, time series, textual, and image. • (2011 onwards) • • •, e.g. For machine learning, using native or non-native libraries. See also [ ] • • • • • • • Notes [ ].

Major AI textbooks [ ] See also the •; (2004), (5th ed.), The Benjamin/Cummings Publishing Company, Inc., • (1998), Artificial Intelligence: A New Synthesis, Morgan Kaufmann Publishers, •; (2003), (2nd ed.), Upper Saddle River, New Jersey: Prentice Hall, •;; (1998),, New York: Oxford University Press, • (1984), Artificial Intelligence, Reading, Massachusetts: Addison-Wesley, History of AI [ ] • (1993), AI: The Tumultuous Search for Artificial Intelligence, New York, NY: BasicBooks, • (2004), (2nd ed.), Natick, MA: A.