Reading List for Knowledge Representation and Reasoning

- AIMA1e Ch.2 (skim)

A general introduction to intelligent agents, the kinds of environments they work in, and the role of knowledge in agent design - AIMA2e Ch.7.1-7.4

Knowledge-based agents, logic in general, and propositional logic in particular, all illustrated in the wumpus world.

* Required reading*

- AIMA2e Ch.7.5-7.6

Covers algorithms for propositional theorem-proving (resolution, forward and backward chaining) and model-checking (DPLL and WalkSAT). - Stephen A. Cook and David G. Mitchell, ``Finding Hard Instances of the Satsifiability Problem: A Survey.''
[
**ps.gz**,**pdf.gz**] In Du, D., Gu, J., and Pardalos, P. (Eds.),*Satisfiability Problem: Theory and Applications*. American Mathematical Society, 1997.

Analysis of progress in propositional satisfiability algorithms and in our understanding of the hardness of randomly generated problems, including discussion of the phase transition phenomenon.

- U. Schoning, ``A probabilistic algorithm for k-SAT and constraint satisfaction problems.''
[
**ps.gz**,**pdf.gz**] In*FOCS-99*.

Describes a simple stochastic satisfiability algorithm that is easy to analyze and runs in (4/3)^n time; the algorithm uses repeated solution attempts from random initial states and ends each attemnpt after 3n steps. - Evgeny Dantsin, Andreas Goerdt, Edward A. Hirsch, Ravi Kannan, Jon Kleinberg, Christos Papadimitriou, Prabhakar Raghavan, Uwe Schoning,
``A deterministic (2-2/(k+1))^n algorithm for k-SAT based on local search.''
[
**ps.gz**,**pdf.gz**]*Theoretical Computer Science*, to appear.

A deterministic derivative of Schoning's algorithm; does a hillclimbing search within each of a set of balls that cover the entire space. - R. Bayardo and R. Schrag, ``Using CSP Look-Back Techniques to Solve Real-World SAT Instances.''
[
**ps.gz**,**pdf.gz**] In*AAAI-97*.

Methods for improving on DPLL-style backtracking search for SAT by caching partial results. - Selman, B., Kautz, H., and McAllester, D., ``Ten Challenges in Propositional Reasoning and Search.''
[
**ps.gz**,**pdf.gz**] In*Proc. IJCAI-97*.

Research challenges, both practical and theoretical, arising from the apparent success of simple stochastic algorithms for SAT.

- AIMA2e, Ch. 7.7

Propositional agents based on theorem-proving and sequential Boolean circuits. - Stanley J. Rosenschein and Leslie Pack Kaelbling,
``A Situated View of Representation and Control.''
[
**ps.gz**,**pdf.gz**]*Artificial Intelligence*, 73(1-2), 149-173, 1995.

A broad introduction to a research agenda aimed at a science of situated agent design based on compiling declarative environment descriptions into sequential circuits that provably meet specifications for information-tracking and goal achievement.

- AIMA2e, Ch. 8

Syntax and semantics of first-order logic, along with many simple examples of its use. - KRR, Chs. 2, 3

Similar content to AIMA2e Ch.8, with a running example (soap-opera world).

- AIMA2e, Ch. 9

Inference by propositionalization; theorem-proving by resolution, including completeness; forward and backward chaining with definite clauses, and logic programming. - Stickel, M. E., ``A Prolog technology theorem prover: a new exposition and implementation in Prolog.''
[
**ps.gz**,**pdf.gz**] Technical Note 464, Artificial Intelligence Center, SRI International, Menlo Park, California, June 1989.

Explains how to modify Prolog so that it is sound and complete, using model-elimination theorem proving; retains the advantages of compilation and efficient implementation. This is the tech report version that includes full code listings; pages 1-20 are the paper itself.

- AIMA1e, Ch. 7.6

Situation calculus as a basic tool for reasoning about actions in first-order logic. - AIMA2e, Ch. 11.1, 11.2, 11.5

Planning algorithms; planning by reduction to propositional model-finding. - Kautz, H., McAllester, D., and Selman, B., ``Encoding plans in propositional logic.''
[
**ps.gz**,**pdf.gz**] In*Proc. Fifth Int'l Conf. on Principles of Knowledge Representation and Reasoning (KR '96)*, Boston, MA, Morgan Kaufmann, 1996.

Describes how to represent planning problems in propositional logic, such that every model corresponds to a valid plan and vice versa. An excellent example of applied KR and the KR debugging cycle.

- Thielscher, M., ``Towards State Update Axioms: Reifying Successor State Axioms.''
[
**ps.gz**,**pdf.gz**] In*Proc. Logics in Artificial Intelligence. European Workshop, JELIA '98*, Dagstuhl, Germany, Springer Verlag, 1998.

Description of solutions to the representational and inferential frame problems, including the fluent calculus. - M. Ernst, T. Millstein, and D. Weld, ``Automatic SAT-Compilation of Planning Problems.''
[
**ps.gz**,**pdf.gz**] In*Proc. IJCAI-97*, Nagoya, Japan, 1997.

Supplements the analysis of Kautz and Selman with a fully automatic SAT compiler for planning problems and empirical analysis of the solution cost with various encodings.

- AIMA2e, Ch. 12.3, 12.4

Basic descriptions of logical decision problems under nondeterminism and partial observability, and a discussion of state-space conditional planning. - P. Bertoli, A. Cimatti, M. Roveri, P. Traverso, ``Planning in Nondeterministic Domains Under Partial Observability via
Symbolic Model Checking.''
[
**ps.gz**,**pdf.gz**] In*Proc. IJCAI-017*, Seattle, 2001.

Extremely fast conditional planning using OBDDs to represent belief states.

- R. E. Bryant, ``Graph-Based Algorithms for Boolean Function Manipulation.''
[
**ps.gz**,**pdf.gz**] IEEE Transactions on Computers, C-35(8) 677-691, 1986.

The basic paper on Ordered Binary Decision Diagrams (OBDDs), a canonical form for propositional logic. - R. E. Bryant, ``Symbolic Boolean Manipulation with Ordered Binary Decision Diagrams.''
[
**ps.gz**,**pdf.gz**] ACM Computing Surveys, 24(3), 293-318, 1992.

A somewhat more up-to-date and readable paper on OBDDs. - Henrik Reif Andersen, ``An Introduction to Binary Decision Diagrams."
[
**ps.gz**,**pdf.gz**] Lecture notes, 1997.

A very thorough set of lecture notes on OBDDs, including a proof of canonicality.

- AIMA1e, Ch. 8.4, 8.5

The organization of a globally consistent ontology for general knowledge representation.

- T. Berners-Lee, J. Hendler, and O. Lassila, ``The Semantic Web.''
[
**html**] In*Scientific American*, May 1st, 2001.

A nontechnical introduction to the Semantic Web proposal, in which Web pages are marked up with logical annotations. - T. Berners-Lee, ``Semantic Web Road Map.''
[
**html**]

Technical overview of the Semantic Web architecture. - F. van Harmelen and D. Fensel, ``Practical Knowledge Representation on the Web.''
[
**ps.gz**,**pdf.gz**] IJCAI 99 Workshop on Intelligent Information Integration, Stockholm, 1999.

A short and incisive survey of various proposals for how to insert formal logical content into Web pages. - A. Levy and D. Weld, ``Intelligent Internet Systems.''
[
**ps.gz**,**pdf.gz**]*Artificial Intelligence*, 118(1-2), 1-14, 2000.

A comprehensive introduction to the general topic of AI and database applied to information representation, extraction, and integration on the Web.

- For XML, see, in decreasing order of brevity,
- The minimalist survival guide to XML, by the DAML+OIL authors.
- Bonnie SooHoo, XML Tutorial 1: Well-Formed XML Documents. In webreview.com Aug. 4, 2000.
- W3C, Extensible Markup Language (XML).

- For RDF, see, in decreasing order of brevity,
- The minimalist survival guide to RDF, by the DAML+OIL authors.
- O. Lassila and R. Swick, Resource Description Framework (RDF) Model and Syntax Specification. W3C Recommendation document, February, 1999.
- W3C, Resource Description Framework.

- T. Berners-Lee, ``What the Semantic Web can represent.''
[
**html**]

What the Semantic Web is*not*-- responses to possible criticisms of the proposal. - I. Horrocks, F. van Harmelen, and P. Patel-Schneider, Eds., ``DAML+OIL.''
[
**html**]

The DARPA Agent Markup Language and the Ontology Interchange Language -- somewhat expressive representation languages with fully worked out logical semantics. I suggest starting with the**example walkthru**.

- AIMA2e, Ch.13

Introduction to uncertain reasoning, probability theory, probability modelling, and the importance of independence and conditional independence.. - AIMA2e, Ch.14.1-3

Bayesian networks: syntax, semantics, construction, representation of conditional ditributions.

- AIMA2e, Ch.14.4-5

Exact inference in BNs using variable elimination; approximate inference using Monte Carlo methods - Michael Wellman, ``Fundamental concepts of qualitative probabilistic networks.''
*Artificial Intelligence*, 44(3), 257-303, 1990. (Hardcopy)

A non-numeric abstraction of Bayesian networks that allows inferences based on monotonic influence relations between variables.

* Required reading*

- AIMA2e, Ch. 15

Representing and reasoning about probabilistic temporal processes, including hidden Markov models, Kalman filters, and dynamic Bayesian networks.

- Dieter Fox, Sebastian Thrun, Wolfram Burgard, Frank Dellaert,
``Particle filters for mobile robot localization.''
[
**ps.gz**,**pdf.gz**] In A. Doucet, N. de Freitas and N.J. Gordon (eds),*Sequential Monte Carlo Methods in Practice*, Springer-Verlag, 2001.

Explains "Markov localization", otherwise known as filtering to estimate position, as used in robotics. Explains how to apply particle filtering to approximate a complex posterior distribution over space. - Hanna Pasula, Stuart Russell, Michael Ostland, and Ya'acov Ritov,
``Tracking many objects with many sensors.''
[
**ps.gz**,**pdf.gz**] In*Proc. IJCAI-99*, Stockholm, 1999.

Describes the problem of "data association" which arises when observations are made of many different objects at each time step, and there is uncertainty about which is which. Uses MCMC on the space of possible matchings among objects. Extended description of an application to traffic surveillance.

- G. Zweig and S. Russell, ``Speech Recognition with Dynamic Bayesian Networks.''
[
**ps.gz**,**pdf.gz**] In*Proc. AAAI-98*, Madison, Wisconsin: AAAI Press, 1998.

Shows how to use dynamic Bayesian networks for speech recognition, with better results than analogous HMM models. - Kevin Murphy and Stuart Russell,
``Rao-Blackwellised Particle Filtering for Dynamic Bayesian Networks.''
[
**ps.gz**,**pdf.gz**] In A. Doucet, N. de Freitas and N.J. Gordon (eds),*Sequential Monte Carlo Methods in Practice*, Springer-Verlag, 2001.

Shows how to scale up particle filtering to very large state spaces by sampling a subset of the variables and performing exact computation for the remainder; application to combined localization and map learning. - X. Boyen and D. Koller, ``Exploiting the architecture of dynamic systems.''
[
**ps.gz**,**pdf.gz**] In*Proceedings of the Sixteenth National Conference on Artificial Intelligence (AAAI-99)*, Orlando, FL, 1999.

An approximate filtering algorithm based on factoring the state into weakly interacting components.

- AIMA2e, Ch.14.6

The basic ideas underlying first-order probabilistic languages, and a simple example. - Daphne Koller and Avi Pfeffer,
``Probabilistic frame-based systems.''
[
**ps.gz**,**pdf.gz**] In*Proc. AAAI-98*, Stockholm, 1998.

Syntax and semantics for an early relational probabilistic language. - Avi Pfeffer, Daphne Koller, Brian Milch, Ken Takusagawa,
``SPOOK: A system for probabilistic object-oriented knowledge representation.''
[
**ps.gz**,**pdf.gz**] In*Proc. UAI-99*, Stockholm, 1999.

Includes an extended example and discussion of efficient inference methods for RPMs. - Avi Pfeffer and Daphne Koller,
``Semantics and inference for recursive probability models.''
[
**ps.gz**,**pdf.gz**] In*Proc. AAAI-00*, Austin, TX, 2000.

Some cases where the space of models is infinite, but posteriors can still be calculated in closed form. - Hanna Pasula and Stuart Russell,
``Approximate inference for first-order probabilistic languages.''
[
**ps.gz**,**pdf.gz**] In*Proc. IJCAI-01*, Seattle, WA, 2001.

Examines the case of uncertainty about relations and about identity of objects; uses MCMC to sample the space of possible relational structures and equivalence relations among objects.

- AIMA1e, Ch. 16 (or AIMA2e Ch. 16 if available)

Basics of decision theory: preferences, utility, optimal decisions, value of information, multiattribute utility. - Pierfrancesco La Mura, ``Game networks.''
[
**ps.gz**,**pdf.gz**] In*Proc. UAI-00*, Austin, TX, 2000.

This paper and the next two all present variations on the theme of factoring the representation of a game in much the same way that Bayesian networks factor the representation of a joint distribution. - Daphne Koller and Brian Milch,
``Multi-agent influence diagrams for representing and solving games.''
[
**ps.gz**,**pdf.gz**] In*Proc. IJCAI-01*, Seattle, WA, 2001. - Michael Kearns, Michael Littman, and Satinder Singh,
``Graphical models for game theory.''
[
**ps.gz**,**pdf.gz**] In*Proc. UAI-01*, Seattle, WA, 2001.