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.

Artificial Intelligence Informed Search and Exploration Readings: Chapter 4 of Russell & Norvig. Artificial Intelligence – p.1/52.

• 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. 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.

CS 541: Artificial Intelligence Planning CS 541: Artificial Intelligence Planning Instructors:,, and. Time: Tuesday and Thursday, 3:30 - 4:50 PM Location: THH 114 Course Description Planning is a key ability for intelligent systems, increasing their autonomy and flexibility through the construction of sequences of actions to achieve their goals. It has been an area of research in artificial intelligence for over three decades. Planning techniques have been applied in a variety of tasks including robotics, process planning, web-based information gathering, autonomous agents and spacecraft mission control. Planning involves the representation of actions and world models, reasoning about the effects of actions, and techniques for efficiently searching the space of possible plans. This course will focus on the basic foundations and techniques in planning and survey a variety of planning systems and approaches.

The class will be run as a lecture course with hands-on experience with state-of-the-art planning systems. Topics covered in the course will include: action and plan representation, reactive systems, hierarchical and abstraction planning, case-based planning, machine learning in planning, multi-agent planning, interacting with the environment, planning under uncertainty, and recent applications such as web service composition and workflow construction on the computational Grid. Prerequisites: CS561a (Introduction to AI), or by permission from the instructors. Here are links to the and versions of this course.

Sample exams Here are for the midterm. Here is a from the course that was given in Fall 2000. Here is a from that course.

Projects is information about projects for the course, including deadlines. Homeworks The first homework assignment is available. It is due on Sept 18th. Discussion Group Share your ideas and questions in the discussion group. Schedule Notes: Suggested Readings are for student enrichment, and students will not be tested on those readings' content. Introduction Aug 26: Action and plan representations, historical overview, STRIPS (Blythe) • Class Slides Planning Approaches Aug 28: Plan generation and causal-link planning 1 (Ambite) • Class Slides • Required Reading (also for Sep 02): Dan Weld, 'An Introduction to Least-Commitment Planning';, AI Magazine 1994 • Suggested Reading (also for Sep 02): J.S.

Penberthy and D. Weld 'UCPOP: A Sound, Complete, Partial-Order Planner for ADL,' Proceedings of KR-92, 103-114, Cambridge, MA, October 1992 Sep 02: Plan generation and causal-link planning 2 (Ambite) • Class Slides • planner Sep 04: Plan graph search (Ambite) • Class Slides • Required Reading: A. Furst, 'Fast Planning Through Planning Graph Analysis', Artificial Intelligence, 90:281--300 (1997). (USC students should have acccess to the original AI journal. If you can't access the original, get the from the authors's page. I had some problems viewing it with Ghostview on Linux, but it prints fine.

Let me know if you have problems printing-- Jose-Luis). • Required Reading: Jana Koehler, Bernhard Nebel, Jorg Hoffman and Yannis Dimopoulos. 'Extending planning graphs to an ADL subset', European Conference on Planning (ECP) 1997. • planner Sep 09: Planning as satisfiability, Planning as constraint satisfaction (Ambite) • Class Slides • Required Reading: Henry Kautz and Bart Selman. Pushing the envelope: Planning, propositional logic, and stochastic search.

In AAAI 1996. • Required Reading: Henry Kautz and Bart Selman.

Unifying SAT-based and Graph-based Planning. • planner • Required Reading: Minh Binh Do & Subbarao Kambhampati. Solving planning-graph by compiling it into CSP. • Suggested Reading: M.

Millstein and D. Weld 'Automatic SAT-Compilation of Planning Problems' IJCAI 1997. • Suggested Reading: Minh Binh Do & Subbarao Kambhampati. Planning as Constraint Satisfaction: Solving the planning graph by compiling it into CSP.

Artificial Intelligence 132 (2001) 151-182 • Sep 11: Planning as model checking, OBDD (Ambite) • Suggested Reading: F. Giunchiglia, P. Planning as Model Checking. In Proceeding of the Fifth European Conference on Planning (ECP' 1999). LNAI, Springer-Verlag. • Suggested Reading: Randal E.

Graph-Based Algorithms for Boolean Function Manipulation. IEEE Transactions on Computers, Vol. 8, August, 1986, pp. • Class Slides Sep 16: Planning on a computational grid (Blythe) • Required reading: J.

Kesselman, A. The role of planning in grid compution. In Proceedings of the first International Conference on Automated Planning and Scheduling, (ICAPS 2003).. • Required reading: B. Srivastava and J. Web service composition - current solutions and open problems.

In Proceedings of the ICAPS 03 workshop on Planning for Web Services, • Suggested reading: E. Gil, C, Kesselman, G. Lazzarini, A. Cavanaugh, S. Mapping Abstract Complex Workflows onto Grid Environments Journal of Grid Computing, Vol. 1, pp 9 - 23 • If you are interested, you can also check out the papers at the. • Class slides Sep 18: Hierarchical task network (HTN) planning (Ambite) • Class Slides • Required reading: K.

Hendler, and D. UMCP: A sound and complete procedure for hierarchical task-network planning.

Proceedings of the International Conference on AI Planning Systems (AIPS), pp. 249-254, June 1994 • Required reading: D. Lotem, and H. SHOP: Simple hierarchical ordered planner. Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI), pp. Morgan Kaufmann Publishers, Jul 31-August 6 1999. • Suggested reading: A.

Barrett and D. Weld 'Task-Decomposition via Plan Parsing,' Proceedings of AAAI-94, Seattle, WA, July 1994. • Suggested reading: S. 'A comparative analysis of partial order planning and task reduction planning' SIGART Bulletin, Special Section on Evaluating Plans, Planners and Planning agents, Vol. 1, January, 1995. Monitor Eye Protection Software Free Download. • Suggested reading: Austin Tate, 'Generating project networks', IJCAI-1977. Reprinted in 'Readings in Planning' James Allen, James Hendler, and Austin Tate, editors.

Morgan Kaufmann, 1990. • The planner. Sep 23: Planning using temporal logics (Blythe) • Required reading: Bacchus and Kabanza, 'Using temporal logics to express search control knowledge for planning', AIJ Skip sections 7.2 - 7.6, inclusive. • Suggested reading: Sections 7.2 - 7.6 of the above • • from a talk by Fahiem Bacchus, in pdf. Slides 31 - 66 are particularly relevant to the class. Sep 25: Heuristic search planning (Blythe) • Required reading: Blai Bonet and Hector Geffner. 'Planning As Heuristic Search'.

Artificial Intelligence 129 (2001). • Class notes:.

Plan Representations Sep 30: Knowledge representation for planning, ontologies, description logics (Gil) • Required reading: Yolanda Gil. 'A (Very Short) Introduction to Description Logics'. • Required reading: Yolanda Gil.

'Plan Representation and Reasoning with Description Logics'. • Suggested reading: A. Ankolekar et al., 'DAML-S: Semantic Markup for Web Services,' submitted for publication in The Emerging Semantic Web. Oct 02: Reasoning about time: temporal reasoning and scheduling (Blythe) • Required reading: Gregg Rabideau, Russell Knight, Steve Chien, Alex Fukunaga, Anita Govindjee. 'Iterative repair planning for spacecraft operations using the ASPEN system' • Required reading: D.

'Temporal Planning with Mutual Exclusion Reasoning.' • Class notes:. Controlling Search Oct 07: Complexity of planning problems (Ambite) • Class Slides • Required Reading: Kutluhan Erol, Dana Nau, and V.S. 'Complexity, Decidability and Undecidability Results for Domain-Independent Planning', Artificial Intelligence Journal, Vol. 75-88, July, 1995. • Required Reading: Section 1 of: Tom Bylander, 'The Computational Complexity of Propositional STRIPS Planning,' Artificial Intelligence, 69:165-204, 1994.

• Suggested Reading: All of: Tom Bylander, 'The Computational Complexity of Propositional STRIPS Planning,' Artificial Intelligence, 69:165-204, 1994. Oct 09: Abstraction, macros, hierarchical planning (Knoblock) • Required Reading: Craig A. 'Automatically generating abstractions for planning', Artificial Intelligence, 68(2), 1994. • Suggested Reading: Richard E. 'Planning as search: A quantitative approach'. Artificial Intelligence, 33(1):65-88, 1987.

Reprinted in Readings in Planning, edited by Allen, Hendler, and Tate, pages 566-578. Morgan Kaufman, 1990. Oct 14: Learning search control knowledge & case-based planning (Blythe) • Required Reading: Manuela Veloso, Jaime Carbonell, Alicia Perez, Daniel Borrajo, Eugene Fink and Jim Blythe. 'Integrating Planning and Learning: the Prodigy Architecture', Journal of Theoretical and Experimental AI, 7(1), 1995.

• Required Reading: Manuela Veloso. 'Flexible Strategy Learning: Analogical Replay of Problem Solving Episodes.' In Proceedings of AAAI-94. • Class slides: Some of these slides are from at CMU.

I also used Manuela's slides to discuss analogical reasoning. Oct 16: Distributed & multi-agent planning (Gil) • Required Reading: Edmund H. Distributed problem solving and planning. Stepankova, and R.

Trappl (eds.), Multiagent Systems and Applications: Selected tutorial papers from the Ninth ECCAI Advanced Course (ACAI 2001) and AgentLink's Third European Agent Systems Summer School (EASSS 2001), pages 118-149, Springer-Verlag Lecture Notes in AI 2086, Berlin 2001. Oct 21: Learning from an external environment (Wei-Min Shen) Oct 23: Midterm Oct 28: Planning and execution (Ambite) • Required Reading: Chapter 13. Artificial Intelligence: A Modern Approach. Russel & Norvig. • Required Reading: Oren Etzioni, Steve Hanks, Daniel Weld, Denise Draper, Neal Lesh, Mike Williamson (1992) 'An Approach to Planning with Incomplete Information'. Proceedings of the 3rd International Conference on Principles of Knowledge Representation and Reasoning.

• Suggested Reading: Ambros-Ingerson, J. & Steel, S (1988) Integrating planning execution and monitoring. In Proceedings of the seventh national conference on artificial intelligence (AAAI 88), Saint Paul, MN, (pp. • Suggested Reading: Golden, K., Etzioni, O.

Planning with Execution and Incomplete Information, UW Technical Report TR96-01-09, February 1996. • Suggested Reading: Golden, K. 'Leap before you look: Information Gathering in the PUCCINI planner', Proceedings of the International Conference on AI Planning Systems (AIPS-98). • Class Slides Oct 30: Reactive systems (Gil) • Required Reading: M. Georgeff and A. Reactive reasoning and planning. In Proceedings of the Sixth National Conference on Artificial Intelligence (AAAI-87), pages 677-682, Seattle, WA, 1987.

( ) • Suggested Reading: Firby, J 'Task Networks for Controlling Continuous Processes', Proceedings of Artificial Intelligence Planning conference, 1994. ( ) • Suggested Reading: Simmons, R. 'Structured Control for Autonomous Robots', IEEE Transactions on Robotics and Automation, Feb 1994. (, ) • Suggested Reading: Zilberstein, S. 'Using Anytime Algorithms in Intelligent Systems', AI Magazine, 1996. • Class notes:. Wolfenstein The New Order Serial Key Download Free. Planning Under Uncertainty Nov 04: Probabilistic planning (Blythe) • Required reading: Blythe, J., 'Decision-Theoretic Planning', AI Magazine, Volume 20, Number 2, Summer 1999, pages 1 to 15 only () • Slides: and (but the PDF has a problem with the slide on the POP algorithm).

Nov 06: Planning in training simulation environments (Jon Gratch, ICT) Nov 11: Probabilistic planning II, exogenous events (Blythe) • Required reading: Blythe, J., 'Event-based decompositions for reasoning about external change in planners', AIPS 1996 (). • Slides: (). Nov 13: Planning and decision theory, Markov decision processes (Blythe) • Required reading: Exploiting Structure in Policy Construction. Boutilier, Dearden and Goldszmidt, IJCAI 95 () • Slides: (). Nov 18: Planning in space (guest speaker:, NASA JPL) • Required reading: Re-read the ASPEN paper from the October 2nd class on temporal reasoning and scheduling: Gregg Rabideau, Russell Knight, Steve Chien, Alex Fukunaga, Anita Govindjee. 'Iterative repair planning for spacecraft operations using the ASPEN system' • suggested readings: The three featured publications on the.

Nov 20: Mixed-initiative planning (Gil) • Required reading: Myers, K. Abductive Completion of Plan Sketches, in Proceedings of the Fourteenth National Conference on Artificial Intelligence, AAAI Press, 1997. () • Required reading: James Allen and George Ferguson, 'Human-Machine Collaborative Planning', to appear in Proceedings of the Third International NASA Workshop on Planning and Scheduling for Space, Houston, TX, October 27-29, 2002. () • Suggested reading: Myers, K. And Morley, D.

Human Directability of Agents, in Proceedings of the First International Conference on Knowledge Capture, Victoria, B.C., 2001. • Suggested reading: Myers, K.

And Jarvis, P. And Tyson, W. And Wolverton, M. A Mixed-initiative Framework for Robust Plan Sketching, in Thirteenth International Conference on Automated Planning and Scheduling (ICAPS-03), 2003.

• Slides: (). Nov 25: Final Review • Jim's slides:,. • Yolanda's slides:,.

• Jose Luis's slides:,. Dec 02: Student presentations of course projects 1 Dec 04: Student presentations of course projects 2 Dec 16: Final exam.