Belief Revision and Progression of KBs in the Epistemic Situation Calculus
|
|
- Homer Pitts
- 6 years ago
- Views:
Transcription
1 Belief Revision and Progression of KBs in the Epistemic Situation Calculus Christoph Schwering 1 Gerhard Lakemeyer 1 Maurice Pagnucco 2 1 RWTH Aachen University, Germany 2 University of New South Wales, Australia
2 2 / 10 Actions and Beliefs Robot is holding a box, does not know what is in it, but 1. believes it is not fragile and not metallic 2. considers fragility more plausible than it being metallic 3. knows it is not broken yet
3 2 / 10 Actions and Beliefs Robot is holding a box, does not know what is in it, but 1. believes it is not fragile and not metallic 2. considers fragility more plausible than it being metallic 3. knows it is not broken yet Drop!
4 2 / 10 Actions and Beliefs Robot is holding a box, does not know what is in it, but 1. believes it is not fragile and not metallic 2. considers fragility more plausible than it being metallic 3. knows it is not broken yet Clink!
5 2 / 10 Actions and Beliefs Robot is holding a box, does not know what is in it, but 1. believes it is not fragile and not metallic 2. considers fragility more plausible than it being metallic 3. knows it is not broken yet How to reason about this?
6 2 / 10 Actions and Beliefs Robot is holding a box, does not know what is in it, but 1. believes it is not fragile and not metallic 2. considers fragility more plausible than it being metallic 3. knows it is not broken yet If 1 3 is all it believed initially, what is all it believes now? Disclaimer: talk includes improvements over paper (journal version in preparation)
7 3 / 10 Logic for Actions and Beliefs First-order logic with modalities: α holds after action A [A]α α holds forever α if φ held, ψ would hold B(φ ψ) Bψ all we believe is φ i ψ i O{φ 1 ψ 1,, φ m ψ m } before forgetting P, O P { }
8 3 / 10 Logic for Actions and Beliefs First-order logic with modalities: α holds after action A [A]α α holds forever α if φ held, ψ would hold B(φ ψ) Bψ all we believe is φ i ψ i O{φ 1 ψ 1,, φ m ψ m } before forgetting P, O P { } Semantics: worlds specify initial and future truth of fluents w = B []B
9 3 / 10 Logic for Actions and Beliefs First-order logic with modalities: α holds after action A [A]α α holds forever α if φ held, ψ would hold B(φ ψ) Bψ all we believe is φ i ψ i O{φ 1 ψ 1,, φ m ψ m } before forgetting P, O P { } Semantics: possible worlds ranked by plausibilities e = B B []B B e = [][]BB (due to revision)
10 4 / 10 All We Believe Only-believing uniquely determines belief structure Related to Levesque s only-knowing, Pearl s Z-Ordering Subsumes only-knowing α by conditional α Theorem: Unique-Model Property O{φ 1 ψ 1,, φ m ψ m } has unique model if φ i, ψ i are obj. Theorem generalizes for O P {φ 1 ψ 1,, φ m ψ m }
11 All We Believe Only-believing uniquely determines belief structure Related to Levesque s only-knowing, Pearl s Z-Ordering Subsumes only-knowing α by conditional α Theorem: Unique-Model Property O{φ 1 ψ 1,, φ m ψ m } has unique model if φ i, ψ i are obj. Usually all we believe is a Basic Action Theory (BAT) with initial beliefs Σ bel knowledge about dynamics physical effect (successor-state axioms due to Reiter): a. [a]b a = F B epistemic effect (action A leads to revision by IF (A)): a. IF (a) (a = B M ) 4 / 10
12 All We Believe O{ F M, F M M, B, dynamic axioms} believes it is not fragile and not metallic considers fragility more plausible than metallic knows it is not broken yet B F M B M B 4 / 10
13 Progression of an Epistemic State 5 / 10 B F M B M B Initially e = B B
14 Progression of an Epistemic State 5 / 10 B F M (B F ) M (B F ) After progression still e = B B
15 Progression of an Epistemic State 5 / 10 B F M (B F ) M (B F ) Natural revision by B M promotes B-worlds to the top
16 Progression of an Epistemic State 5 / 10 B F M (B F ) M (B F ) After revision (e ) (B M ) = BB
17 Progression of an Epistemic State 5 / 10 B F M (B F ) M (B F ) After progression e = BB
18 Progression of a BAT by a Physical Action 6 / 10 Similar to Lin and Reiter s progression Let A have no epistemic effect Let F be fluents of BAT with axioms [a]f ( x) γ F Let P be new predicates Beliefs after doing A Σ bel A = Σ bel F P { ( x.f ( x) γ F a F A P) F F} Substitute P for F to capture pre-a beliefs Assert x.f ( x) γ F a F A P to set post-a beliefs
19 7 / 10 Progression of a BAT by an Epistemic Action Let A have no physical effect Progression Σ bel A = Σ bel IF (A) Let = {φ ψ Σ bel OΣ bel = B(α φ ψ)} Let P be a new predicate Beliefs after promoting the most-plausible α-worlds Σ bel α = { P } { (P α) } { (φ P ψ) φ ψ } {φ P ψ φ ψ Σ bel }
20 P -worlds are the most plausible worlds P -worlds represent promoted α-worlds P -worlds represent original belief structure 7 / 10 Progression of a BAT by an Epistemic Action Let A have no physical effect Progression Σ bel A = Σ bel IF (A) Let = {φ ψ Σ bel OΣ bel = B(α φ ψ)} Let P be a new predicate Beliefs after promoting the most-plausible α-worlds Σ bel α = { P } { (P α) } { (φ P ψ) φ ψ } {φ P ψ φ ψ Σ bel }
21 8 / 10 Progression of a BAT Briefly: BAT progression matches semantic progression Theorem: Progression #1 = OΣ [A]O P {P } (Σ A) Roughly: If all we believe is Σ, then all we believe after A is Σ A. Theorem: Progression #2 = OΣ [A]α iff = O P {P } (Σ A) α Roughly: Σ A entails the same beliefs as Σ has after A.
22 9 / 10 Belief Revision Postulates Alchourron Gärdenfors Makinson (AGM) hold Darwiche Pearl (DP) hold with a little restriction on DP2 Original DP2 is violated because we cannot recover from an inconsistent state Nayak Pagnucco Peppas (NPP) violated because the order matters in natural revision
23 10 / 10 Conclusion and Ongoing/Future Work Situation calculus plus natural revision Belief progression using only-believing Other revision schemes, e.g., lexicographic Projection by regression Elimination of (nested) beliefs similar to our AAAI-15 paper When is progression first-order-definable? Feasible subclass Implementation based on Lakemeyer & Levesque, KR-14
24 Appendix
25 All We Believe Believe it is not fragile and not metallic F M Fragility is more plausible than metallic F M M Know that it is not broken B Know dynamic axioms (omitted) 1. ( F M ) (F M M ) (B ) B F M 2. (F M M ) (B ) B M 3. (B ) B 2 / 2
Iterated Belief Change in the Situation Calculus
Iterated Belief Change in the Situation Calculus Steven Shapiro a,, Maurice Pagnucco b, Yves Lespérance c, Hector J. Levesque a a Department of Computer Science, University of Toronto, Toronto, ON M5S
More informationIterated Belief Change: A Transition System Approach
Iterated Belief Change: A Transition System Approach Aaron Hunter and James P. Delgrande School of Computing Science Simon Fraser University Burnaby, BC, Canada {amhunter, jim}@cs.sfu.ca Abstract We use
More informationMULTI-AGENT ONLY-KNOWING
MULTI-AGENT ONLY-KNOWING Gerhard Lakemeyer Computer Science, RWTH Aachen University Germany AI, Logic, and Epistemic Planning, Copenhagen October 3, 2013 Joint work with Vaishak Belle Contents of this
More informationAction, Belief Change and the Frame Problem: A Fluent Calculus Approach
Action, Belief Change and the Frame Problem: A Fluent Calculus Approach Richard B. Scherl Computer Science Department Monmouth University West Long Brach, NJ 07764 U.S.A. email: rscherl@monmouth.edu Abstract
More informationA Semantic Approach for Iterated Revision in Possibilistic Logic
Proceedings of the Twenty-Third AAAI Conference on Artificial Intelligence (2008) A Semantic Approach for Iterated Revision in Possibilistic Logic Guilin Qi AIFB Universität Karlsruhe D-76128 Karlsruhe,
More informationLaboratoire d'informatique Fondamentale de Lille
Laboratoire d'informatique Fondamentale de Lille Publication IT { 314 Operators with Memory for Iterated Revision Sebastien Konieczny mai 1998 c L.I.F.L. { U.S.T.L. LABORATOIRE D'INFORMATIQUE FONDAMENTALE
More informationRepresenting Beliefs in the Fluent Calculus
Representing Beliefs in the Fluent Calculus Yi Jin and Michael Thielscher 1 Abstract. Action formalisms like the uent calculus have been developed to endow logic-based agents with the abilities to reason
More informationAn Action Description Language for Iterated Belief Change
An Action Description Language for Iterated Belief Change Aaron Hunter and James P. Delgrande Simon Fraser University Burnaby, BC, Canada {amhunter, jim}@cs.sfu.ca Abstract We are interested in the belief
More informationDefinability of Horn Revision from Horn Contraction
Definability of Horn Revision from Horn Contraction Zhiqiang Zhuang 1,2 Maurice Pagnucco 2,3,4 Yan Zhang 1 1 School of Computing, Engineering, and Mathematics, University of Western Sydney, Australia 2
More informationRational Partial Choice Functions and Their Application to Belief Revision
Rational Partial Choice Functions and Their Application to Belief Revision Jianbing Ma 1, Weiru Liu 2 and Didier Dubois 2,3 1 School of Design, Engineering and Computing, Bournemouth University, Bournemouth,
More informationInverse Resolution as Belief Change
Inverse Resolution as Belief Change Maurice Pagnucco ARC Centre of Excel. for Autonomous Sys. School of Comp. Science and Engineering The University of New South Wales Sydney, NSW, 2052, Australia. Email:
More informationSituations, si! Situation terms, no!
Situations, si! Situation terms, no! Gerhard Lakemeyer Dept. of Computer Science RWTH Aachen 52056 Aachen Germany gerhard@cs.rwth-aachen.de Hector J. Levesque Dept. of Computer Science University of Toronto
More informationArbitrary Announcements in Propositional Belief Revision
Arbitrary Announcements in Propositional Belief Revision Aaron Hunter British Columbia Institute of Technology Burnaby, Canada aaron hunter@bcitca Francois Schwarzentruber ENS Rennes Bruz, France francoisschwarzentruber@ens-rennesfr
More informationBelief Change in the Context of Fallible Actions and Observations
Belief Change in the Context of Fallible Actions and Observations Aaron Hunter and James P. Delgrande School of Computing Science Faculty of Applied Sciences Simon Fraser University Burnaby, BC, Canada
More informationOn First-Order Definability and Computability of Progression for Local-Effect Actions and Beyond
On First-Order Definability and Computability of Progression for Local-Effect Actions and Beyond Yongmei Liu Dept. of Computer Science Sun Yat-sen University Guangzhou 510275, China ymliu@mail.sysu.edu.cn
More informationPartial Meet Revision and Contraction in Logic Programs
Partial Meet Revision and Contraction in Logic Programs Sebastian Binnewies and Zhiqiang Zhuang and Kewen Wang School of Information and Communication Technology Griffith University, QLD, Australia {s.binnewies;
More informationOn the Progression of Situation Calculus Basic Action Theories: Resolving a 10-year-old Conjecture
On the Progression of Situation Calculus Basic Action Theories: Resolving a 10-year-old Conjecture Stavros Vassos and Hector J Levesque Department of Computer Science University of Toronto Toronto, ON,
More informationDL-Lite Contraction and Revision
Journal of Artificial Intelligence Research 56 (2016) 329-378 Submitted 12/15; published 06/16 DL-Lite Contraction and Revision Zhiqiang Zhuang Institute for Integrated and Intelligent Systems Griffith
More informationThe Situation Calculus and Golog
and A Tutorial Gerhard Lakemeyer Dept. of Computer Science RWTH Aachen University Germany 2nd Hybris Workshop, Freiburg, May 27 28, 2013 Outline 1 2 3 May 28, 2013 2 / 34 Knowledge-Based Agents Example:
More informationWhat is a Default Priority?
What is a Default Priority? Craig Boutilier Department of Computer Science University of British Columbia Vancouver, British Columbia CANADA, V6T 1Z2 email: cebly@cs.ubc.ca Abstract The notion of default
More informationAn Extension of BDI CTL with Functional Dependencies and Components
An Extension of BDI CTL with Functional Dependencies and Components Mehdi Dastani 1 and Leendert van der Torre 2 1 Institute of Information and Computer Sciences, Utrecht University mehdi@cs.uu.nl 2 Department
More informationDalal s Revision without Hamming Distance
Dalal s Revision without Hamming Distance Pilar Pozos-Parra 1, Weiru Liu 2, and Laurent Perrussel 3 1 University of Tabasco, Mexico pilar.pozos@ujat.mx 2 Queen s University Belfast, UK w.liu@qub.ac.uk
More informationarxiv: v2 [cs.lo] 27 Jan 2015
Dynamics of Belief: Abduction, Horn Knowledge Base And Database Updates Radhakrishnan Delhibabu arxiv:1501.06206v2 [cs.lo] 27 Jan 2015 Informatik 5, Knowledge-Based Systems Group RWTH Aachen, Germany delhibabu@kbsg.rwth-aachen.de
More informationBounded revision: Two-dimensional belief change between conservative and moderate revision. Hans Rott
Bounded revision: Two-dimensional belief change between conservative and moderate revision Hans Rott Department of Philosophy, University of Regensburg hans.rott@psk.uni-r.de Abstract In this paper I present
More informationSteps Towards Commonsense-Driven Belief Revision in the Event Calculus
Steps Towards Commonsense-Driven Belief Revision in the Event Calculus Nikoleta Tsampanaki, Giorgos Flouris, Theodore Patkos Institute of Computer Science, FORTH, Greece {tsaban, fgeo, patkos}@ics.forth.gr
More informationTractable Reasoning with Incomplete First-Order Knowledge in Dynamic Systems with Context-Dependent Actions
Tractable Reasoning with Incomplete First-Order Knowledge in Dynamic Systems with Context-Dependent Actions Yongmei Liu and Hector J. Levesque Department of Computer Science University of Toronto Toronto,
More informationBOUNDED REVISION: TWO-DIMENSIONAL BELIEF CHANGE BETWEEN CONSERVATISM AND MODERATION. Hans Rott
BOUNDED REVISION: TWO-DIMENSIONAL BELIEF CHANGE BETWEEN CONSERVATISM AND MODERATION Hans Rott Department of Philosophy, University of Regensburg hans.rott@psk.uni-regensburg.de ABSTRACT: In this paper
More informationDoxastic Logic. Michael Caie
Doxastic Logic Michael Caie There are at least three natural ways of interpreting the object of study of doxastic logic. On one construal, doxastic logic studies certain general features of the doxastic
More informationStrong Syntax Splitting for Iterated Belief Revision
Strong Syntax Splitting for Iterated Belief Revision Gabriele Kern-Isberner 1 and Gerhard Brewka 2 1 Fakultät für Informatik, Technische Universität Dortmund, Germany 2 Institut für Informatik, Universität
More informationA First-Order Logic of Probability and Only Knowing in Unbounded Domains
A First-Order Logic of Probability and Only Knowing in Unbounded Domains Vaishak Belle Dept. of Computer Science KU Leuven Belgium vaishak@cs.kuleuven.be Gerhard Lakemeyer Dept. of Computer Science RWTH
More informationReconstructing an Agent s Epistemic State from Observations
Reconstructing an Agent s Epistemic State from Observations Richard Booth Macquarie University Dept. of Computing Sydney NSW 2109 Australia rbooth@ics.mq.edu.au Alexander Nittka University of Leipzig Dept.
More informationA Bad Day Surfing is Better than a Good Day Working: How to Revise a Total Preorder
A Bad Day Surfing is Better than a Good Day Working: How to Revise a Total Preorder Richard Booth Faculty of Informatics Mahasarakham University Mahasarakham 44150, Thailand richard.b@msu.ac.th Thomas
More informationINTRODUCTION TO NONMONOTONIC REASONING
Faculty of Computer Science Chair of Automata Theory INTRODUCTION TO NONMONOTONIC REASONING Anni-Yasmin Turhan Dresden, WS 2017/18 About the Course Course Material Book "Nonmonotonic Reasoning" by Grigoris
More informationHorn Clause Contraction Functions
Journal of Artificial Intelligence Research 48 (2013) xxx-xxx Submitted 04/13; published 09/13 Horn Clause Contraction Functions James P. Delgrande School of Computing Science, Simon Fraser University,
More informationConflict-Based Belief Revision Operators in Possibilistic Logic
Conflict-Based Belief Revision Operators in Possibilistic Logic Author Qi, Guilin, Wang, Kewen Published 2012 Conference Title Proceedings of the Twenty-Sixth AAAI Conference on Artificial Intelligence
More informationOn iterated revision in the AGM framework
On iterated revision in the AGM framework Andreas Herzig, Sébastien Konieczny, Laurent Perrussel Institut de Recherche en Informatique de Toulouse 118 route de Narbonne - 31062 Toulouse - France {herzig,konieczny,perrussel}@irit.fr
More informationBelief Contraction for the Description Logic EL
Belief Contraction for the Description Logic EL Zhi Qiang Zhuang and Maurice Pagnucco ARC Centre of Excellence for Autonomous Systems and National ICT Australia, School of Computer Science and Engineering,
More informationOn the Progression of Knowledge in the Situation Calculus
On the Progression of Knowledge in the Situation Calculus Yongmei Liu Department of Computer Science Sun Yat-sen University Guangzhou, China Joint work with Ximing Wen KRW-2012, Guiyang June 17, 2012 Y.
More informationBelief revision: A vade-mecum
Belief revision: A vade-mecum Peter Gärdenfors Lund University Cognitive Science, Kungshuset, Lundagård, S 223 50 LUND, Sweden Abstract. This paper contains a brief survey of the area of belief revision
More informationBase Revision in Description Logics - Preliminary Results
Base Revision in Description Logics - Preliminary Results Márcio Moretto Ribeiro and Renata Wassermann Institute of Mathematics and Statistics University of São Paulo Brazil, {marciomr,renata}@ime.usp.br
More informationA framework for managing uncertain inputs: an axiomization of rewarding
A framework for managing uncertain inputs: an axiomization of rewarding Jianbing Ma, Weiru Liu School of Electronics, Electrical Engineering and Computer Science, Queen s University Belfast, Belfast BT7
More informationAgent Communication and Belief Change
Agent Communication and Belief Change Satoshi Tojo JAIST November 30, 2014 Satoshi Tojo (JAIST) Agent Communication and Belief Change November 30, 2014 1 / 34 .1 Introduction.2 Introspective Agent.3 I
More informationA Unifying Semantics for Belief Change
A Unifying Semantics for Belief Change C0300 Abstract. Many belief change formalisms employ plausibility orderings over the set of possible worlds to determine how the beliefs of an agent ought to be modified
More informationA New Approach to Knowledge Base Revision in DL-Lite
A New Approach to Knowledge Base Revision in DL-Lite Zhe Wang and Kewen Wang and Rodney Topor School of ICT, Griffith University Nathan, QLD 4111, Australia Abstract Revising knowledge bases (KBs) in description
More informationOn the Decidability of Verifying LTL Properties of GOLOG Programs
On the Decidability of Verifying LTL Properties of GOLOG Programs Benjamin Zarrieß Theoretical Computer Science TU Dresden, Germany zarriess@tcs.inf.tu-dresden.de Jens Claßen Knowledge-Based Systems Group
More informationAxiomatic characterization of the AGM theory of belief revision in a temporal logic
Artificial Intelligence 171 (2007) 144 160 www.elsevier.com/locate/artint Axiomatic characterization of the AGM theory of belief revision in a temporal logic Giacomo Bonanno 1 Department of Economics,
More informationKnowledge Forgetting in Circumscription: A Preliminary Report
Knowledge Forgetting in Circumscription: A Preliminary Report Yisong Wang Department of Computer Science, Guizhou University, 550025, China Kewen Wang and Zhe Wang and Zhiqiang Zhuang School of Information
More informationProperties of Knowledge Forgetting
Properties of Knowledge Forgetting Yan Zhang and Yi Zhou Intelligent Systems Laboratory University of Western Sydney, Australia E-mail: {yan,yzhou}@scm.uws.edu.au Abstract In this paper we propose a formal
More informationBelief Update for Proper Epistemic Knowledge Bases
Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence (IJCAI-16) Belief Update for Proper Epistemic Knowledge Bases Tim Miller and Christian Muise University of Melbourne,
More informationCausality and Minimal Change Demystified
Causality and Minimal Change Demystified Maurice Pagnucco Computational Reasoning Group Department of Computing Division of ICS Macquarie University NSW, 210, Australia morri@ics.mq.edu.au Abstract The
More informationConditional Logic and Belief Revision
Conditional Logic and Belief Revision Ginger Schultheis (vks@mit.edu) and David Boylan (dboylan@mit.edu) January 2017 History The formal study of belief revision grew out out of two research traditions:
More informationTowards an Integration of Golog and Planning
Towards an Integration of Golog and Planning Jens Claßen + and Patrick Eyerich and Gerhard Lakemeyer + and Bernhard Nebel + Department of Computer Science, RWTH Aachen, 52056 Aachen, Germany classen gerhard
More informationLogics for Belief as Maximally Plausible Possibility
Logics for Belief as Maximally Plausible Possibility Giacomo Bonanno Department of Economics, University of California, Davis, USA gfbonanno@ucdavis.edu Abstract We consider a basic logic with two primitive
More informationbrels: A System for the Integration of Knowledge Bases
brels: A System for the Integration of Knowledge Bases Paolo Liberatore and Marco Schaerf Dipartimento di Informatica e Sistemistica Università di Roma La Sapienza Via Salaria 113, I-00198, Roma, Italy
More informationReasoning with Inconsistent and Uncertain Ontologies
Reasoning with Inconsistent and Uncertain Ontologies Guilin Qi Southeast University China gqi@seu.edu.cn Reasoning Web 2012 September 05, 2012 Outline Probabilistic logic vs possibilistic logic Probabilistic
More informationComment on Leitgeb s Stability Theory of Belief
Comment on Leitgeb s Stability Theory of Belief Hanti Lin Kevin T. Kelly Carnegie Mellon University {hantil, kk3n}@andrew.cmu.edu Hannes Leitgeb s stability theory of belief provides three synchronic constraints
More informationProgression and Verification of Situation Calculus Agents with Bounded Beliefs
Progression and Verification of Situation Calculus Agents with Bounded Beliefs Giuseppe De Giacomo DIAG, Sapienza Università di Roma Roma, Italy degiacomo@dis.uniroma1.it Yves Lespérance EECS York University
More informationA sequential reversible belief revision method based on polynomials
From: AAAI-99 Proceedings. Copyright 1999, AAAI (www.aaai.org). All rights reserved. A sequential reversible belief revision method based on polynomials Salem Benferhat and Didier Dubois IRIT, universitd
More informationIntegrating State Constraints and Obligations in Situation Calculus
Integrating State Constraints and Obligations in Situation Calculus Robert Demolombe ONERA-Toulouse 2, Avenue Edouard Belin BP 4025, 31055 Toulouse Cedex 4, France. Robert.Demolombe@cert.fr Pilar Pozos
More informationRevising General Knowledge Bases in Description Logics
Revising General Knowledge Bases in Description Logics Zhe Wang and Kewen Wang and Rodney Topor Griffith University, Australia Abstract Revising knowledge bases (KBs) in description logics (DLs) in a syntax-independent
More informationBase Belief Change and Optimized Recovery 1
Base Belief Change and Optimized Recovery 1 Frances Johnson 2 and Stuart C. Shapiro University at Buffalo, CSE Dept., Buffalo, NY, USA flj shapiro@cse.buffalo.edu Abstract. Optimized Recovery (OR) adds
More informationKnowledge, Belief, and Noisy Sensing in the Situation Calculus. Patricio D. Simari
Knowledge, Belief, and Noisy Sensing in the Situation Calculus by Patricio D. Simari A thesis submitted in conformity with the requirements for the degree of Master of Science Graduate Department of Computer
More informationFallbacks and push-ons
Fallbacks and push-ons Krister Segerberg September 22, 2006 1 Background Grove showed in effect that, in the theory of belief change initiated by Alchourrón, Gärdenfors and Makinson, belief states may
More informationLTCS Report. On the Decidability of Verifying LTL Properties of Golog Programs (Extended Version) LTCS-Report 13-10
Technische Universität Dresden Institute for Theoretical Computer Science Chair for Automata Theory LTCS Report On the Decidability of Verifying LTL Properties of Golog Programs (Extended Version) Benjamin
More informationRevision of DL-Lite Knowledge Bases
Revision of DL-Lite Knowledge Bases Zhe Wang, Kewen Wang, and Rodney Topor Griffith University, Australia Abstract. We address the revision problem for knowledge bases (KBs) in Description Logics (DLs).
More informationBelief Contraction and Revision over DL-Lite TBoxes
Belief Contraction and Revision over DL-Lite TBoxes Zhiqiang Zhuang 1 Zhe Wang 1 Kewen Wang 1 Guilin Qi 2 1 School of Information and Communication Technology, Griffith University, Australia 2 School of
More informationA Semantics for ADL as Progression in the Situation Calculus
A Semantics for ADL as Progression in the Situation Calculus Jens Claßen and Gerhard Lakemeyer Department of Computer Science RWTH Aachen 52056 Aachen Germany classen gerhard @cs.rwth-aachen.de Abstract
More informationNext Steps in Propositional Horn Contraction
Next Steps in Propositional Horn Contraction Richard Booth Mahasarakham University Thailand richard.b@msu.ac.th Thomas Meyer Meraka Institute, CSIR and School of Computer Science University of Kwazulu-Natal
More informationReinforcement Belief Revision
Reinforcement Belief Revision Yi Jin School of Computing Science Simon Fraser University yij@cs.sfu.ca Michael Thielscher Department of Computer Science Dresden University of Technology mit@inf.tu-dresden.de
More informationProjection using Regression and Sensors
Projection using Regression and Sensors Giuseppe De Giacomo Dipartimento di Informatica e Sistemistica Università di Roma La Sapienza Via Salaria 11, 00198 Roma, Italy degiacomo@dis.uniroma1.it Hector
More informationModal Logic & Kripke Semantics
Modal Logic & Kripke Semantics Modal logic allows one to model possible truths reasoning what is possible and what is simply not possible when we don t have complete knowledge. For example: it is possible
More informationÖzgür L. Özçep Ontology Change 2
Özgür L. Özçep Ontology Change 2 Lecture 10: Revision for Ontology Change 24 January, 2017 Foundations of Ontologies and Databases for Information Systems CS5130 (Winter 16/17) Recap of Lecture 9 Considered
More informationA Logic for evision a &iv eri
From: AAAI-92 Proceedings. Copyright 1992, AAAI (www.aaai.org). All rights reserved. A Logic for evision a &iv eri Craig Boutilier Department of Computer Science University of British Columbia Vancouver,
More informationAdding Modal Operators to the Action Language A
Adding Modal Operators to the Action Language A Aaron Hunter Simon Fraser University Burnaby, B.C. Canada V5A 1S6 amhunter@cs.sfu.ca Abstract The action language A is a simple high-level language for describing
More informationAn Egalitarist Fusion of Incommensurable Ranked Belief Bases under Constraints
An Egalitarist Fusion of Incommensurable Ranked Belief Bases under Constraints Salem Benferhat and Sylvain Lagrue and Julien Rossit CRIL - Université d Artois Faculté des Sciences Jean Perrin Rue Jean
More informationOn the Conservativity and Stability of Ontology-Revision Operators Based on Reinterpretation
On the Conservativity and Stability of Ontology-Revision Operators Based on Reinterpretation Özgür L. Özçep and Carola Eschenbach Department for Informatics (AB WSV), MIN Faculty, University of Hamburg
More informationOn Ability to Autonomously Execute Agent Programs with Sensing
On Ability to Autonomously Execute Agent Programs with Sensing Sebastian Sardiña Dept. of Computer Science University of Toronto ssardina@cs.toronto.edu Yves Lespérance Dept. of Computer Science York University
More information14. Actions. KR & R Brachman & Levesque Situation calculus
14. Actions KR & R Brachman & Levesque 2005 236 Situation calculus The situation calculus is a dialect of FOL for representing dynamically changing worlds in which all changes are the result of named actions.
More informationPrime Forms and Minimal Change in Propositional Belief Bases
Annals of Mathematics and Artificial Intelligence manuscript No. (will be inserted by the editor) Prime Forms and Minimal Change in Propositional Belief Bases J. Marchi G. Bittencourt L. Perrussel Received:
More informationContraction and Revision over DL-Lite TBoxes
Contraction and Revision over DL-Lite TBoxes Zhiqiang Zhuang 1 Zhe Wang 1 Kewen Wang 1 Guilin Qi 2,3 1 School of Information and Communication Technology, Griffith University, Australia 2 School of Computer
More informationarxiv: v1 [cs.lo] 27 Mar 2015
Revisable Justified Belief: Preliminary Report Alexandru Baltag Bryan Renne Sonja Smets arxiv:1503.08141v1 [cs.lo] 27 Mar 2015 Abstract The theory CDL of Conditional Doxastic Logic is the single-agent
More informationInconsistencies, Negations and Changes in Ontologies
Inconsistencies, Negations and Changes in Ontologies Giorgos Flouris 1 Zhisheng Huang 2,3 Jeff Z. Pan 4 Dimitris Plexousakis 1 Holger Wache 2 1 Institute of Computer Science, FORTH, Heraklion, Greece emails:
More informationKnowing Whether in Proper Epistemic Knowledge Bases
Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence (AAAI-16) Knowing Whether in Proper Epistemic Knowledge Bases Tim Miller, Paolo Felli, Christian Muise, Adrian R. Pearce, Liz Sonenberg
More informationProvability as a Modal Operator with the models of PA as the Worlds
Provability as a Modal Operator with the models of PA as the Worlds by Marcello Herreshoff marce110@stanford.edu May 20, 2011 Abstract This paper introduces a propositional modal model of Gödel-Löb Logic
More informationRanking Theory. Franz Huber Department of Philosophy University of Toronto, Canada
Ranking Theory Franz Huber Department of Philosophy University of Toronto, Canada franz.huber@utoronto.ca http://huber.blogs.chass.utoronto.ca The Open Handbook of Formal Epistemology edited by Richard
More informationAwareness, Negation and Logical Omniscience
Awareness, Negation and Logical Omniscience Zhisheng Huang and Karen Kwast Department of Mathematics and Computer Science University of Amsterdam Plantage Muidergracht 24 1018TV Amsterdam, The Netherlands
More informationReasoning about Conditional Beliefs for the Winograd Schema Challenge
Reasoning about Conditional Beliefs for the Winograd Schema Challenge Denis Golovin and Jens Claßen Knowledge-Based Systems Group RWTH Aachen University Aachen, Germany Christoph Schwering School of Computer
More informationProgression of Situation Calculus Action Theories with Incomplete Information
Progression of Situation Calculus Action Theories with Incomplete Information Stavros Vassos and Hector Levesque Department of Computer Science University of Toronto Toronto, ON, CANADA {stavros,hector}@cs.toronto.edu
More informationMeaning and Reference INTENSIONAL AND MODAL LOGIC. Intensional Logic. Frege: Predicators (general terms) have
INTENSIONAL AND MODAL LOGIC Meaning and Reference Why do we consider extensions to the standard logical language(s)? Requirements of knowledge representation / domain modelling Intensional expressions:
More informationKnowledge representation DATA INFORMATION KNOWLEDGE WISDOM. Figure Relation ship between data, information knowledge and wisdom.
Knowledge representation Introduction Knowledge is the progression that starts with data which s limited utility. Data when processed become information, information when interpreted or evaluated becomes
More informationAction representation and partially observable planning using epistemic logic
Action representation and partially observable planning using epistemic logic Andreas Herzig IRIT/UPS F-31062 Toulouse Cedex France herzig@irit.fr Jerome Lang IRIT/UPS F-31062 Toulouse Cedex France lang@irit.fr
More informationFirst Order Logic: Syntax and Semantics
CS1081 First Order Logic: Syntax and Semantics COMP30412 Sean Bechhofer sean.bechhofer@manchester.ac.uk Problems Propositional logic isn t very expressive As an example, consider p = Scotland won on Saturday
More informationarxiv: v1 [cs.ai] 26 Jul 2018
On Strengthening the Logic of Iterated Belief Revision: Proper Ordinal Interval Operators arxiv:1807.09942v1 [cs.ai] 26 Jul 2018 Richard Booth Cardiff University Cardiff, UK boothr2@cardiff.ac.uk Jake
More informationDescription Logics. Foundations of Propositional Logic. franconi. Enrico Franconi
(1/27) Description Logics Foundations of Propositional Logic Enrico Franconi franconi@cs.man.ac.uk http://www.cs.man.ac.uk/ franconi Department of Computer Science, University of Manchester (2/27) Knowledge
More informationSituation Calculus. Gerald Steinbauer. Institute for Software Technology. Gerald Steinbauer. Situation Calculus - Introduction
Situation Calculus Institute for Software Technology 1 Dates Organizational Issues 10.11.2016 8:45-11:00 (HS i12) lecture and first assignment 17.11.2016 8:45-11:00 (HS i12) lecture and programming assignment
More informationLogicalFoundations ofnegotiation: Outcome, Concession and Adaptation
LogicalFoundations ofnegotiation: Outcome, Concession and Adaptation Thomas Meyer and Norman Foo National ICT Australia School of CSE, UNSW Sydney, Australia tmeyer@cse.unsw.edu.au Rex Kwok Knowledge Systems
More informationA Unifying Framework for Probabilistic Belief Revision
A Unifying Framework for Probabilistic Belief Revision Zhiqiang Zhuang Griffith University z.zhuang@griffith.edu.au James Delgrande Simon Fraser University jim@cs.sfu.ca Abhaya Nayak Macquarie University
More informationTowards Tractable Inference for Resource-Bounded Agents
Towards Tractable Inference for Resource-Bounded Agents Toryn Q. Klassen Sheila A. McIlraith Hector J. Levesque Department of Computer Science University of Toronto Toronto, Ontario, Canada {toryn,sheila,hector}@cs.toronto.edu
More informationCS 4700: Foundations of Artificial Intelligence
CS 4700: Foundations of Artificial Intelligence Bart Selman selman@cs.cornell.edu Module: Knowledge, Reasoning, and Planning Part 2 Logical Agents R&N: Chapter 7 1 Illustrative example: Wumpus World (Somewhat
More informationDistributing Knowledge into Simple Bases
Distributing Knowledge into Simple Bases Adrian Haret and Jean-Guy Mailly and Stefan Woltran Institute of Information Systems TU Wien, Austria {haret,jmailly,woltran}@dbai.tuwien.ac.at Abstract Understanding
More information