Declarative programming for agent applications

This paper introduces the execution model of a declarative programming language intended for agent applications. Features supported by the language include functional and logic programming idioms, higher-order functions, modal computation, probabilistic computation, and some theorem-proving capabilities. The need for these features is motivated and examples are given to illustrate the central ideas.

[1]  Ben Taskar,et al.  Introduction to statistical relational learning , 2007 .

[2]  John Wylie Lloyd,et al.  Foundations of Logic Programming , 1987, Symbolic Computation.

[3]  John W. Lloyd,et al.  Probabilistic modelling, inference and learning using logical theories , 2008, Annals of Mathematics and Artificial Intelligence.

[4]  Leon Henkin,et al.  Completeness in the theory of types , 1950, Journal of Symbolic Logic.

[5]  Stuart J. Russell,et al.  BLOG: Probabilistic Models with Unknown Objects , 2005, IJCAI.

[6]  Alex M. Andrew,et al.  Knowledge in Action: Logical Foundations for Specifying and Implementing Dynamical Systems , 2002 .

[7]  Luis Fariñas del Cerro,et al.  A General Framework for Pattern-Driven Modal Tableaux , 2002, Log. J. IGPL.

[8]  Ben Taskar,et al.  Bayesian Logic Programming: Theory and Tool , 2007 .

[9]  Laks V. S. Lakshmanan,et al.  Modeling Uncertainty in Deductive Databases , 1994, DEXA.

[10]  Yang Xiang,et al.  PROBABILISTIC REASONING IN MULTIAGENT SYSTEMS: A GRAPHICAL MODELS APPROACH, by Yang Xiang, Cambridge University Press, Cambridge, 2002, xii + 294 pp., ISBN 0-521-81308-5 (Hardback, £45.00). , 2002, Robotica.

[11]  Michael Fisher,et al.  MetateM: The Story so Far , 2005, PROMAS.

[12]  F. S. deBoer.  Agent Programming in 3APL , 1999 .

[13]  Radford M. Neal.  Pattern Recognition and Machine Learning , 2007, Technometrics.

[14]  Peyton Jones,et al.  Haskell 98 language and libraries : the revised report , 2003 .

[15]  V. S. Subrahmanian,et al.  Probabilistic Logic Programming , 1992, Inf. Comput..

[16]  Michael Thielscher,et al.  Under Consideration for Publication in Theory and Practice of Logic Programming Flux: a Logic Programming Method for Reasoning Agents , 2003 .

[17]  Daniel Gallin,et al.  Intensional and Higher-Order Modal Logic , 1975 .

[18]  John W. Lloyd,et al.  The Gödel programming language , 1994 .

[19]  L. Goble.  The Blackwell guide to philosophical logic , 2001 .

[20]  Michael I. Jordan.  Graphical Models , 2003 .

[21]  José Júlio Alferes,et al.  Multi-dimensional Dynamic Knowledge Representation , 2001, LPNMR.

[22]  W. Bibel,et al.  Automated deduction : a basis for applications , 1998 .

[23]  Wayne Wobcke,et al.  An agent-based approach to dialogue management in personal assistants , 2005, IUI.

[24]  Jürgen Dix,et al.  Agents dealing with time and uncertainty , 2002, AAMAS '02.

[25]  Richard L. Mendelsohn,et al.  First-Order Modal Logic , 1998 .

[26]  Jörg Flum,et al.  Mathematical logic , 1985, Undergraduate texts in mathematics.

[27]  Peter Norvig,et al.  Artificial Intelligence: A Modern Approach , 1995 .

[28]  Johann Eder,et al.  Logic and Databases , 1992, Advanced Topics in Artificial Intelligence.

[29]  V. S. Subrahmanian,et al.  Hybrid Probabilistic Programs , 2000, J. Log. Program..

[30]  Reinhard Muskens,et al.  Higher order modal logic , 2007, Handbook of Modal Logic.

[31]  David Poole,et al.  First-order probabilistic inference , 2003, IJCAI.

[32]  T. Başar,et al.  A New Approach to Linear Filtering and Prediction Problems , 2001 .

[33]  Jürgen Dix,et al.  IMPACT: A Multi-Agent Framework with Declarative Semantics , 2005, Multi-Agent Programming.

[34]  Simon L. Peyton Jones,et al.  The Implementation of Functional Programming Languages , 1987 .

[35]  M. Fitting.  Types, Tableaus, and Gödel's God , 2002 .

[36]  Leslie Pack Kaelbling,et al.  Logical Particle Filtering , 2007, Probabilistic, Logical and Relational Learning - A Further Synthesis.

[37]  S. Sanner.  First-order Decision-theoretic Planning in Structured Relational Environments , 2008 .

[38]  Linh Anh Nguyen.  Multimodal logic programming , 2006, Theor. Comput. Sci..

[39]  R. Rivest.  Learning Decision Lists , 1987, Machine Learning.

[40]  Martin Giese,et al.  Incremental Closure of Free Variable Tableaux , 2001, IJCAR.

[41]  S. Muggleton.  Stochastic Logic Programs , 1996 .

[42]  Dan Roth,et al.  Lifted First-Order Probabilistic Inference , 2005, IJCAI.

[43]  D. Gabbay,et al.  Many-Dimensional Modal Logics: Theory and Applications , 2003 .

[44]  Gilles Dowek,et al.  Higher-Order Unification and Matching , 2001, Handbook of Automated Reasoning.

[45]  M. Kohlhase.  Higher-Order Automated Theorem Proving , 2008 .

[46]  Norman Ramsey,et al.  Stochastic lambda calculus and monads of probability distributions , 2002, POPL '02.

[47]  Daniel Marcu,et al.  Foundations of a Logical Approach to Agent Programming , 1995, ATAL.

[48]  J. Benthem,et al.  Higher-Order Logic , 2001 .

[49]  Dale A. Miller,et al.  Proofs in Higher-Order Logic , 1983 .

[50]  Eric McCreath,et al.  Improving the learning rate by inducing a transition model , 2004, Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems, 2004. AAMAS 2004..

[51]  John W. Lloyd,et al.  Programming in an Integrated Functional and Logic Language , 1999, J. Funct. Log. Program..

[52]  Wanli Ma,et al.  An Overview of Temporal and Modal Logic Programming , 1994, ICTL.

[53]  X. Jin.  Factor graphs and the Sum-Product Algorithm , 2002 .

[54]  Rafael H. Bordini,et al.  Jason and the Golden Fleece of Agent-Oriented Programming , 2005, Multi-Agent Programming.

[55]  Keith L. Clark,et al.  Go! — A Multi-Paradigm Programming Language for Implementing Multi-Threaded Agents , 2004, Annals of Mathematics and Artificial Intelligence.

[56]  Luc De Raedt,et al.  Bayesian Logic Programming: Theory and Tool , 2007 .

[57]  Joseph Y. Halpern.  An Analysis of First-Order Logics of Probability , 1989, IJCAI.

[58]  John W. Lloyd,et al.  Learning Comprehensible Theories from Structured Data , 2002, Machine Learning Summer School.

[59]  Gérard P. Huet,et al.  A Unification Algorithm for Typed lambda-Calculus , 1975, Theor. Comput. Sci..

[60]  Lise Getoor,et al.  Learning Probabilistic Relational Models , 1999, IJCAI.

[61]  John W. Lloyd,et al.  Probabilistic reasoning in a classical logic , 2009, J. Appl. Log..

[62]  Sylvie Thiébaux,et al.  Exploiting First-Order Regression in Inductive Policy Selection , 2004, UAI.

[63]  Gopalan Nadathur,et al.  Higher-Order Logic Programming , 1986, ICLP.

[64]  Luc De Raedt,et al.  Probabilistic logic learning , 2003, SKDD.

[65]  Keith L. Clark,et al.  Negation as Failure , 1987, Logic and Data Bases.

[66]  Alonzo Church,et al.  A formulation of the simple theory of types , 1940, Journal of Symbolic Logic.

[67]  Ronald Fagin,et al.  Reasoning about knowledge , 1995 .

[68]  Andrei Voronkov,et al.  The design and implementation of VAMPIRE , 2002, AI Commun..

[69]  Frank Pfenning,et al.  Functional programming with names and necessity , 2004 .

[70]  Kristian Kersting,et al.  Lifted Probabilistic Inference , 2012, ECAI.

[71]  A. S. Roa,et al.  AgentSpeak(L): BDI agents speak out in a logical computable language , 1996 .

[72]  Simon J. Godsill,et al.  On sequential simulation-based methods for Bayesian filtering , 1998 .

[73]  Nevin L. Zhang,et al.  A simple approach to Bayesian network computations , 1994 .

[74]  D. Dickson.  The story so far… , 2000, Nature.

[75]  Anh Nguyen,et al.  An adaptive plan-based dialogue agent: integrating learning into a BDI architecture , 2006, AAMAS '06.

[76]  M. Hanus,et al.  Curry: An Integrated Functional Logic Language , 2003 .

[77]  Scott Sanner,et al.  Practical solution techniques for first-order MDPs , 2009, Artif. Intell..

[78]  Ben Taskar,et al.  Introduction to Statistical Relational Learning (Adaptive Computation and Machine Learning) , 2007 .

[79]  Michael Winikoff,et al.  Learning Within the BDI Framework: An Empirical Analysis , 2005, KES.

[80]  Ronald Fagin,et al.  Reasoning about knowledge and probability , 1988, JACM.

[81]  Ralf Hinze,et al.  Haskell 98 — A Non−strict‚ Purely Functional Language , 1999 .

[82]  Christopher M. Bishop,et al.  Pattern Recognition and Machine Learning (Information Science and Statistics) , 2006 .

[83]  Joshua B. Tenenbaum,et al.  Church: a language for generative models , 2008, UAI.

[84]  Peter Norvig,et al.  Artificial intelligence - a modern approach, 2nd Edition , 2003, Prentice Hall series in artificial intelligence.

[85]  Jürgen Dix,et al.  Multi-Agent Programming: Languages, Tools and Applications , 2009 .

[86]  Melvin Fitting,et al.  First-Order Logic and Automated Theorem Proving , 1990, Graduate Texts in Computer Science.

[87]  Eyal Amir,et al.  Sampling First Order Logical Particles , 2008, UAI.

[88]  Frank Wolter,et al.  Handbook of Modal Logic , 2007, Studies in logic and practical reasoning.

[89]  Simon Peyton Jones,et al.  The Implementation of Functional Programming Languages (Prentice-hall International Series in Computer Science) , 1987 .

[90]  John W. Lloyd,et al.  Learning Modal Theories , 2007, ILP.

[91]  John W. Lloyd,et al.  The Go¨del programming language , 1994 .

[92]  Michael Mendler,et al.  Special issue: Modalities in type theory , 2001, Math. Struct. Comput. Sci..

[93]  John N. Tsitsiklis,et al.  Neuro-Dynamic Programming , 1996, Encyclopedia of Machine Learning.

[94]  Michael Hanus,et al.  The Integration of Functions into Logic Programming: From Theory to Practice , 1994, J. Log. Program..

[95]  William M. Farmer,et al.  The seven virtues of simple type theory , 2008, J. Appl. Log..

[96]  João Leite,et al.  A Survey of Programming Languages and Platforms for Multi-Agent Systems , 2006, Informatica.

[97]  Tobias Nipkow,et al.  A Proof Assistant for Higher-Order Logic , 2002 .

[98]  Manolis Gergatsoulis,et al.  Temporal and Modal Logic Programming Languages , 2002 .

[99]  Mark Reynolds.  D. M. Gabbay, A. Kurucz, F. Wolter, and M. Zakharyaschev. Many-dimensional modal logics: theory and applications . Studies in Logic and the Foundations of Mathematics, vol. 148. Elsevier, Amsterdam, xiv + 747 pp. , 2005 .

[100]  Michael Wooldridge,et al.  Reasoning about rational agents , 2000, Intelligent robots and autonomous agents.

[101]  Daniel Leivant,et al.  Higher order logic , 1994, Handbook of Logic in Artificial Intelligence and Logic Programming.

[102]  De Raedt,et al.  Advances in Inductive Logic Programming , 1996 .

[103]  J. W. Lloyd,et al.  Foundations of logic programming; (2nd extended ed.) , 1987 .

[104]  Dov M. Gabbay,et al.  Handbook of logic in artificial intelligence and logic programming (vol. 1) , 1993 .

[105]  John W. Lloyd,et al.  Reflections on Agent Beliefs , 2007, DALT.

[106]  J. W. Lloyd,et al.  Logic for Learning , 2003, Cognitive Technologies.

[107]  Neil J. Gordon,et al.  A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking , 2002, IEEE Trans. Signal Process..

[108]  John W. Lloyd,et al.  Personalisation for user agents , 2005, AAMAS '05.

[109]  John W. Lloyd,et al.  Symbolic Learning for Adaptive Agents , 2003 .

[110]  John W. Lloyd,et al.  Probabilistic and Logical Beliefs , 2008, LADS.

[111]  Henrik Grosskreutz,et al.  Probabilistic, Temporal Projections in ConGolog , 1999 .

[112]  P. Gehler,et al.  An introduction to graphical models , 2001 .

[113]  Peter B. Andrews.  An introduction to mathematical logic and type theory - to truth through proof , 1986, Computer science and applied mathematics.

[114]  José Júlio Alferes,et al.  MINERVA - A Dynamic Logic Programming Agent Architecture , 2001, ATAL.

[115]  Rafael H. Bordini,et al.  Multi-Agent Programming: Languages, Platforms and Applications , 2005, Multi-Agent Programming.

[116]  J. W. Lloyd.  Logic and Learning , 2003 .

[117]  Martin L. Puterman,et al.  Markov Decision Processes: Discrete Stochastic Dynamic Programming , 1994 .

[118]  Michael Hanus,et al.  Multi-paradigm Declarative Languages , 2007, ICLP.

[119]  Alex M. Andrew,et al.  Logic for Learning: Learning Comprehensible Theories from Structured Data , 2004 .