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REFLECTIONS on LOGIC PROGRAMMING and NONMONOTONIC REASONING by JACK MINKER UNIVERSITY OF MARYLAND
Thank Gerhard Brewka, Chitta Baral, John Schlipf First LPNMR 1991 Banquet Address Overview of LP and NMR I do not have time to provide an overview of the field now, so I plan to highlight the significant events that have occurred. This does not mean that the work I do not cover was not significant. So I hope some of my friends will be tolerant of this talk if I omit their work. Thank Baral, Brewka, Gelfond, Levesque Lifschitz, Leone, Niemela, Straub, Swift, Truszczynski, Warren, Zaniolo
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INTRODUCTION BEGINNINGS LOGIC PROGRAMMING
DISJUNCTIVE LOGIC PROGRAMMING NONMONOTONIC REASONING LP and NMR IMPLEMENTATIONS RECENT DEVELOPMENTS APPLICATIONS SUMMARY and CONCLUSIONS
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BEGINNINGS McCARTHY Common Sense Reasoning (1959)
DEFINED ‘OLDEST’ PROBLEM IN AI LIFSCHITZ, McCAIN, REMOLINA, TURNER (2000) CCALC Situations, Actions, Causal Laws (1963) GOLOG (LEVESQUE, REITER (1997)) McCarthy and Hayes Philosophical Problems and Frame Axioms (1969) Seeming Need for Large Number of Axioms to Represent Changes REITER (1980, 1991), SHANAHAN (1997) Robinson (1965) Resolution Principle for Automated Theorem Proving Minsky Frame Paper and Critique of Logic in AI (1975) McCarthy – One of founders of AI, NMR, and founder of Logic-Based AI. Recognized of the importance of the situation calculus, his approach to commonsense reasoning and his discussion of philosophical problems and the need for frame axioms with Pat Hayes showed great foresight. Lifschitz: ``The discovery of the frame problem and the invention of nonmonotonic formalisms that are capable of solving it may be the most significant events so far in the history of research on reasoning about actions.’’ Robinson – Basis of work in Kowalski and Kuehner’s SLD used in Prolog. Founding E-i-C of LP Journal, now the TPLP Minsky critique in that he recognized that first order logic could not model many realistic situations. May have been the first to recognize the nonmonotonic nature of a large part of AI.
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MINSKY’S CRITIQUE OF LOGIC
``LOGICAL’’ REASONING IS NOT FLEXIBLE FOR THINKING INCONSISTENT DATA CANNOT BE HANDLED FEASIBILITY OF REPRESENTING KNOWLEDGE BY SMALL ``TRUE’’ PROPOSITIONS IS DOUBTFUL SEPARATION OF KNOWLEDGE AND RULES IS TOO RADICAL LOGIC IS MONOTONIC PROCEDURAL DESCRIPTIONS OVER DECLARATIVE DESCRIPTIONS Of this list Minsky got the first and the 5th items right.
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LOGIC PROGRAMMING BEGINNINGS
HODES (1966) GREEN (1969) HEWITT (1969) THNOT Operator PLANNER ELCOCK (1971) ABSYS and ABSET – Declarative Languages HAYES (1973) Computation and Deduction COLMERAUER (1973) PROLOG – NOT Operator Kowalski/Kuehner SLD for Horn Clauses WARREN, PEREIRA, PEREIRA (1977) EDINBURGH PROLOG Competitive With LISP Kowalski visited Colmerauer in Marseilles and as a result incorporated used SLD resolution in Prolog. The NOT operator gave Prolog a nonmonotonic capability, just as the THNOT operator by the essentially procedural PLANNER. That is, they had nonmonotonic capabilites. Warren, Pereira and Pereira developed an elegant Prolog system that compared favorably with LISP. The work has led to the field of computational linguistics
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HORN LOGIC PROGRAMMING FOUNDATIONS
HORN CLAUSES p(t1, …, tm) A1, …, An KOWALSKI and KUEHNER –SL Resolution (1971) Descendant of Model Elimination (Loveland 1969) LUSH/SLD (Hill 1974, Apt and Van Emden 1982) KOWALSKI (1974) Van EMDEN and KOWALSKI (1976) FIXPOINT SEMANTICS MODEL THEORY SEMANTICS OPERATIONAL SEMANTICS LOGIC and DATABASES (WORKSHOP 1977, BOOK 1978 Gallaire, Minker) DEDUCTIVE DATABASES REITER (1978) NEGATION (REITER CLOSED WORLD ASSUMPTION) DOMAIN CLOSURE AXIOM UNIQUE NAME AXIOM CLARK (1978) NEGATION (CLARK COMPLETION THEORY COMP(P) – IFF) p(t1, …, tm) A1, …, An p(x1, …, xm) y1… yp (x1 = t1 Λ … Λ xn = tn Λ A1 … Λ An) Clark Equational Theory (CET) LLOYD (1984, 1987) FOUNDATIONS OF LOGIC PROGRAMMING SURVEYS ON NEGATION SHEPHERDSON (1988, 1998) Alan Robinson in his Foreword to the Kakas and Sadri Essays in honor of Bob Kowalski was skeptical about the use of SLD for a programming language. I must also admit that I too was sceptical when I heard Bob give a talk in 1974 and remarked about it to him and he explained that it was not a full theorem prover at which time I underrstood. At a Workshop I helped organize with Gallaire and Nicolas, the formal foundations were laid in the handling of negation. Book Logic and Databases, based on the Workshop was reviewed by David Harel and basically said that the work had nothing to do with databases.
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DISJUNCTIVE LOGIC PROGRAMMING – FIRST STEP
NON HORN CLAUSE (DISJUNCTIVE CLAUSE) P1, …, Pn A1, …, Am THEORY OF NEGATION REITER’s CWA is INCONSISTENT for DISJUNCTION {P v Q} then by CWA, {not P} and {not Q} Minker (1982) GENERALIZED CLOSED WORLD ASSUMPTION MODEL THEORETIC - MINIMAL MODELS {P}, {Q} Positive Truths – True in every minimal model Negative Truths – Not True in any minimal model PROOF THEORETIC Neither Kowalski nor Reiter were interested by including disjunction in the head of Horn clauses. I believe they thought it would be computationally complex and there would be few applications of interest. I did not pursue disjunctive theories for several years until
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STRATIFIED AND NORMAL LOGIC PROGRAMMING
P A1, A2, …An, not B1,…, not Bm {p not q, q p} (not stratified) {p not q, r , q q, not r} rewritten for stratification as {r}, {q q, not r}, {p not q} STRATIFIED LP APT, BLAIR and WALKER (1988) VAN GELDER (1988) PRZYMUSINSKI (1988) PERFECT MODELS NORMAL LP VAN GELDER, ROSS and SCHLIPF (1988) WELL FOUNDED SEMANTICS (WFS) {p not q, q not p} WFS: {p and q are unknown} GELFOND and LIFSCHITZ (1989) STABLE MODEL SEMANTICS Stable models {{p}, {q}} Prolog had a limited capability with respect to negated atoms in the body of a clause. I believe a major step with respect to nonmonotonic reasoning came in the time period At a workshop I organized in Apt Blair and Walker developed the theoretical foundations of stratified LPs, and Przymusinski showed that this was the perfect model. Also in 1988 Van Gelder published two important papers , one on stratified theories and with Ross and Schlipf, they develped the WFS for non-stratified theories. Stratified and WFS opened up the range of theories that could be modeled with logic programs. ABW deal with minimal supported models. Perfect Models are stricter conditions than ABW and apply to disjunctive theories. When restricted to the case of ABW, the two semantics are the same.
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STABLE MODELS GELFOND, LIFSCHITZ (1991)
REDUCT PI of P w.r.t Interpretation I Delete all rules with a negative false literal (w.r.t. I) Delete the negative literals from the bodies of the remaining literals A Stable Model of a program P is an interpretation I such that I is an answer set of PI Several different semantics were proposed for Normal LPs, and GL proposed the stable model semantics. Others were proposed and, – at LPNMR91 banquet I was asked which semantics was the most useful. I replied, it would depend upon what the user wanted with respect to the answer to the problem. A voice from the back of the room modestly stated, STABLE MODEL SEMANTICS. I responded, cautiously, we shall see. Sixteen years later, I must admit, publicly, what I have realized for well over a decade is that VLADIMIR was ALMOST correct, it is ANSWER SET SEMANTICS.
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DISJUNCTIVE LOGIC PROGRAMMING THEORY
P1, P2, …, Pm A1, A2, …, An MINKER and RAJASEKAR (1987) FIXPOINT OPERATOR MODEL THEORY PROOF THEORY LUST/SLI (MINKER, ZANON 1982, LOBO, MINKER,RAJASEKAR 1992) EXTENDED DLP (with Baral, Lobo, Ruiz, Seipel) (Gelfond and Lifschitz 1991) P1, P2, …, Pm A1, A2, …, An, not B1, not B2, …, not Bk Negation in body of clauses SLINF (MINKER, RAJASEKAR 1990) LOBO, MINKER, RAJESEKAR (1992) FOUNDATIONS of DISJUNCTIVE LOGIC PROGRAMMING GELFOND and LIFSCHITZ Classical Negation (1991) Answer Set Semantics (1999) Unfortunately we were not able to incorporate the work by GL on DLP in our book since they discussed the use of stable models. Both Lobo and Rajesekar had graduated and we could not delay the book.
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APPLICATIONS DISJUNCTIVE LP
KNOWLEDGE REPRESENTATION BARAL, GELFOND (1995) BARAL (2002) Knowledge Representation, Reasoning and Declarative Problem Solving OTHER APPLICATIONS 3 Color Problem Hamiltonian Path See Problems in LPNMR07 ASP Contest
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ABDUCTIVE LOGIC PROGRAMMING
ABDUCTION INTRODUCED BY PHILOSOPHER C.S, PIERCE (1955) An Inference Process of Forming a hypothesis that explains given observed phenomena Study of Abduction in LP Introduced in Late 1990s Eshgi, Kowalski, Denecker, Kakas, Mancarella early workers in field Kowalski, Kakas and Toni (1993) ``Abductive Logic Programming’’ Answer Set Programming used as basis for some implementations Performing Abduction in Disjunctive Logic Programming Studied by Eiter, Leone, Mateis, Pfeifer, Scarcello (1998) and by Sato and Inoue who discussed abduction and DLP Mancarella, Sadri, Terreni and Toni (2007 at LPNMR07), discuss the use of CIFF for abductive reasoning with constraints and show that their system compares favorably with A-System, DLV and Smodels Abduction is important for such applications as medical problems and law, as well as many other problems
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NONMONOTONIC THEORIES
CIRCUMSCRIPTION (McCARTHY 1980) DEFAULT REASONING (REITER 1980) AUTOEPISTEMIC REASONING (MOORE 1985) 1980 papers in Artificial Intelligence Journal
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CIRCUMSCRIPTION Let A be a sentence of FOL containing predicate symbol P(x1,…,xn) written as P(x). We write A(Ø) as result for replacing all predicates P in A by the predicate expression Ø. The CIRCUMSCRIPTION OF P IN A(P) is the sentence schema A(Ø) Λ x(Ø(x) P(x)) x(P(x) Ø(x)) (1) LIFSCHITZ: POINTWISE, PRIORITIZED, PARALLEL, INTROSPECTIVE McCarthy’s sentence schema can be regarded as asserting: the only tuples x that satisfy P are those that have to -- assuming the sentence A. Namely, (1) contains a predicate parameter Ø for which we may subsitute an arbitrary predicate expression. Since (1) is an implication, we can assume both conjuncts on the left, and (1) lets us conclude the sentence on the right. The first conjunct A(Ø) expresses the assumption that Ø satisfies the conditions satisfied by P, and the second x(Ø(x) P(x)) expresses the assumption that the entities satisfying Ø are a subset of those that satisfy P. The conclusion asserts the converse of the second conjunct which tells us that in this case, and P must coincide. We write A proves q relative to the predicat P if the sentence q can be obtained by deduction from the result of circumscribing P in A. As we shall see is a nonmonotonic form of inference, which we shall call circumscriptive inference.
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DEFAULT REASONING DEFAULT REASONING DEFAULT RULES : -----
If is true and is consistent with a set of beliefs, then is believed EXTENSIONS TO DEFAULT REASONING DISJUNCTIVE DEFAULTS (GELFOND,LIFSCHITZ, PRZYMUSINSKA, TRUSZCZYNSKI (1991)) :1, …, m 1 | … | n Generalizes the semantics of disjunctive and extended disjunctive databases CONSTRAINED (DELGRAND, SCHAUB, JACKSON (1999)) CUMULATIVE DEFAULT LOGIC (BREWKA (1991)) JUSTIFIED DEFAULT LOGIC (LUKASZIEWICZ (1988)) RATIONAL DEFAULT LOGIC (MIKITIUK, TRUSZCZYNSKI (1988)) DEFAULTS WITH PREFERENCES AND INHERITANCE (DELGRANDE, SCHAUB (2002))
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MODAL THEORIES AUTOEPISTEMIC LOGIC
Modal Logic augments FOL by operators such as B (believes), K (knows) that take sentences as arguments rather than terms. Invented by Hintikka (1962). Kripke (1963) defined semantics of modal logic of knowledge in terms of possible worlds. Moore related modal logic of knowledge to reasoning about knowledge which refers directly to possible worlds in FOL.
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RELATIONSHIPS AE/DEFAULT/CIRCUMSCRIPTION
PERLIS (1988)and LIFSCHITZ (1989) VARIANTS OF CIRCUMSCRIPTION ANALOGOUS TO AEL KONOLIGE (1987) STRENGTHENS AEL TO BE EQUIVALENT TO PROPOSITIONAL FORM OF DEFAULT LOGIC MAREK/TRUSZCZYNSKI (1989) EXTEND WORK OF KONOLIGE MAREK/SUBRAHMANIAN (1989) RELATE FORMAL MODELS OF NORMAL PROGRAMS AND EXPANSIONS OF AE THEORIES
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ADDITIONAL RELATIONSHIPS AE/CIRCUMSCRIPTION/DEFAULT/LP
REITER (1982) FIRST TO RELATE CIRCUMSCRIPTION TO LOGIC PROGRAMMING Marek and Truszczynski (1989) Stable Models for Default Logic GELFOND (1987) GENERAL LOGIC PROGRAMS TRANSLATE TO AEL GELFOND/LIFSCHITZ (1988) STABLE MODEL SEMATICS EQUIVALENT TO TRANSLATION OF LOGIC PROGRAMS TO AEL LIFSCHITZ (1989) AEL, STABLE MODELS AND INTROSPECTIVE CIRCUMSCRIPTION PROVIDE 3 EQUIVALENT DESCRIPTIONS OF PROPOSITIONAL LOGIC PROGRAMS PRZYMUSINSKI (1988) RELATIONSHIPS BETWEEN LP AND NMR EXTENDS AEL TO GENERALIZED AEL AND RELATES AEL TO REITER’S CWA GAEL TO MINKER’S GCWA No general systems were available for AE, CIRC, or NMR. Since there was a direct relationship between NMR Theories and LP/DLP. it was natural to extend LP for normal programs and disjunction. Work started in the 1990s and by the second part of the 1990s a reasonably large number of systems had been implemented. stratification, WFS and disjunction.
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ADDITIONAL RELATIONS Bonatti (1993)
AEL Programs Generalize Ideas in LP Stable, Supported WFS, Fitting’s and Kunen’s Semantics and Abduction can be Captured by AEL Translations Generalized SLDNF and a Generate and Test Method To Provide Sound and Complete Methods for AE Programs Lin, ZHOU (2007) Answer Sets and Circumscription Map Pearce Equilibrium Logic (2001) and Ferraris’s General Logic Programs (2005) to Lin and Shoham’s Knowledge of Justified Assumptions (1992) (a nonmonotonic modal logic that includes as special cases Reiter’s default logic in propositional case and Moore’s AEL). Allows a Mapping from general logic programming to propositional circumscription.
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IMPLEMENTATIONS at LBAI 2000
Niemela, Simon (1997) SMODELS Marek and Truszczynski DeReS Warren, et al. (1999) XSB (Well Founded Models) Eiter, Leone, Mateis, Pfeifer, Scarcello (1997) DLV (Disjunctive Theories) Zaniolo, Arni, Ong (1993) LDL++ All of these systems and several others, were demonstrated at a workshop on Logic-Based AI I organized with McCarthy in 1999 from which the book I edited, Logic-Based AI resulted. I believe that implementations are another milestone in the development of LPNMR. Stimulated work in LPNMR for the past decade and improved efficiency and capabilities I will focus on what has happened since the workshop on some of these efforts.
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IMPLEMENTATIONS at LBAI 2000 (CON’T)
PLANNING TLPlan (Bacchus et al.) GPT (Bonet/Geffner) Blackbox (Kautz/Selman/Huang) CCALC (Lifschitz/McCain/Turner) Golog (Levesque et al.) INDUCTIVE LOGIC PROGRAMMING CPROLOG (Muggleton/Srinivasan) MULTIAGENT APPLICATIONS IMPACT (Subrahmanian et al.)
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NONMONOTONIC REASONING PARADIGM
Use any NMR Theory to Define your Problem Translate the Theory to LP/DLP system Depending upon your translation and whether or not the translation has recursion through negation, select an existing system that best meets your needs Dominant semantics is Answer Set Semantics Implement and Test your System Build Capabilities Using Existing Systems A-Prolog Implemented on Top of Smodels (Gelfond et al.) (2002) GnT Built on Top of Smodels to achieve disjunction It may be possible that the current LP system does not have the capability you need, you may have to figure out how to incorporate it into the system that best meets your need. We do not have to start from scratch to implement a system to handle NMR and we can do it with declarative languages.
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IMPLEMENTATION REPOSITORY
DAGSTUHL INITIATIVE PROPOSAL (1996) Minker Proposed Developing a Database of Information about LP System Implementations and Applications. University of Koblenz developed web site listing systems and applications. (Furbach) 32 SYSTEMS LISTED (Last updated 2000) Applications Page Inaccessible DAGSTUHL INITIATIVE PROPOSAL (2002) Develop infrastructure for benchmarking ASP solvers Environment for submitting and archiving benchmarking problems and instances in which ASP systems can be benchmarked under equal and reproducible conditions, leading to independent results. Asparagus Web Site International Board Assure Continuation and Generate Continued Interest Consider Broadening the Material in the Asparagus Web Site, not necessarily for the contest Information about other nonmonotonic systems (WFS), Successful Real Applications, Cognotive Robotics, Logic Planning Programs, … FIRST INTERNATIONAL CONTEST ASP SYSTEMS LPNMR 07 Evaluation Committee: GEBSER, LIU, NAMASIVAYAN, NEUMANN, TRAUB, TRUSZCZYNSKI SYSTEMS: Asper, Angers; Assat, Hong Kong; Clasp Potsdam; Cmodels, Texas; dlv, Vienna/Rende; gnt, Helsinki; lp2sat, Helsinki; nomore, Potsdam; pbmodels, Kentucky; Smodels, Helsinki 37 problems listed for First Answer Set Programming System Contest THE COMPETITION COMMITTEE HAS AUTHORIZED ME TO ANNOUNCE THE WINNER IS: To be perfectly honest, I do not recall making this recommendation at a talk at Dagstuhl, but found it when I googled for Minker and Dagstuhl. organization. Applications that have been run on systems are also needed. We must show results of the theoretical and implementations that have been achieved. We must publicize contributions of the systems if NMR is to be used in real applications and to help obtain funding agency support.
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First Answer Set Programming System Competition Committee
TO BE ANNOUNCED BY THE First Answer Set Programming System Competition Committee
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SIGNIFICANT DEVELOPMENTS -1
IMPRESSSED BY WORK THAT HAS COMBINED THEORY, COMPLEXITY, IMPLEMENTATION AND EXPERIMENTAL WORK, PRIMARILY ON ANSWER SET PROGRAMMING EXTENSIONS TO ANSWER SET PROGRAMMING - SMODELS Choice Rules, Cardinality and Weight Constraints (NIEMELA, SIMONS 2000) Cardinality Constraint L{a1, …, an, not b1, …, bm}U Cardinality and Weight Constraints are form of AGGREGATES that correspond to COUNT and SUM (first to introduce into non stratified programs) Disjunction capability, GnT, Built on Top of Smodels (~2000) Unfolding Partiality and Disjunctions in Stable Model Semantics (Janhusen, Niemela, Seipel, Simons, You 2006) Develop Implementation methodology for partial & disjunctive stable models where partiality and disjunctions are unfolded Implementation of stable models of normal (disjunction-free) logic programs can be used to compute stable models for disjunctive logic programs They show partial stable models can be captured by total stable models using a simple linear & modular program transformation. Experiments on several classes of problems compares favorably with DLV
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SIGNIFICANT DEVELOPMENTS -2
DLV Generate & Test Paradigm (Eiter, Leone 2002) Disjunctive Rule ``Guesses’’ Solution Candidate S Integrity constraints which check admissibility of S Recursive Aggregates in Disjunctive Logic Programming; Semantics and Complexity (Faber, Leone, Pfeifer 2004) (Faber and Leone ) Enhancing Magic Sets for Disjunctive Datalog (Cumbo, Faber, Greco, Leone) Magic Sets and Data Integration (Faber, Greco, Leone 2007) INFOMIX (Calabria, Roma, Vienna, Warsaw Groups 2005) Data Integration Integrity Constraints over global schema Sound and complete logic-based methods for query answering Deal with incomplete and inconsistent data DLV and disjunctive data Addition of Aggregates has been one of the most relevant enhancements to the language for ASP. Strengthens the modeling power of natural and concise problem solving. Does not affect complexity of the full language, but increases complexity for the non-disjunctive case. Prove that for some classes of programs using aggregates the complexity remains polynomial. Magic Sets have improved the efficiency of applications.
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SIGNIFICANT DEVELOPMENTS - 3
Extensions to Handle Ordered Disjunctions and Inconsistencies, CR-PROLOG2 (Consistency Restoring ) (BALDUCCINI, MELLARKOD 2004) r: A1, …, Ak l1, …, lm, not lm+1, …, not ln r A1 x … x Ak l1, …, lm, not lm+1, …, not ln (introduced by Brewka, Niemela, Syrajnen 2003) cr H + l1, …, lm, not lm+1, …, not ln ``may possibly’’ believe one of the elements of the head if agent has no way to obtain a consistent set of beliefs using regular rules only. Extend ASP to Include Probabilities - Allows Probabilistic Causal Reasoning (BARAL, GELFOND, RUSHTON 2007) Combines ASP with ideas of Judea Pearl Allows reasoning with causal probabilities and probabilistic updates Debated whether AI should be logic-based or probability based. This work indicates that there need not be a dichotomy. Combines ASP with ideas of Judea Pearl, and, allows reasoning with causal probabilities and various forms of probabilistic updates. Denecker and Vennekens are also working in this area.
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SIGNIFICANT DEVELOPMENTS - 4
Loop Formulas (Lin, Zhao 2002) Relationship Between Clark’s Completion and Stable Models Loop formulas are those needed to be added to the Clark completion of the Program to get exact characterization of its stable models Loop {pq, qp} program has a unique answer set comp: {pq, qp} has 2 models {{p}, {q}} Loop formula (p q) false – none of them can be in answer set Serves as new basis to implement stable model semantics (ASSAT) Complete the program Conjoin with loop formulas Invoke SAT solver to find satisfying truth assignments Output truth assignments as stable models of program Completion does not work for programs with loops
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APPLICATIONS ACADEMIC APPLICATIONS – USEFUL FOR TESTING AND INTRODUCING NEW FEATURES (3-COLOR, HAMILTONIAN CIRCUIT, …) NON-ACADEMIC REALISTIC APPLICATIONS NEEDED DEMONSTRATE UTILITY OF LPNMR HANDLE LARGE APPLICATIONS (E.G. INTERFACE WITH SQL SYSTEM) HANDLE PROBLEMS NEEDED by USERS, EFFECTIVE INTERFACES, DEBUGGERS, OPTIMIZERS, HEURISTICS, … TRANSFER TECHNOLOGY TO USER
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NON-ACADEMIC APPLICATIONS -1
XSB (Warren) Ontology Management Work from textual database fields and technical drawings Extracted and inferred attributes of parts from textual database fields so organization could better understand what they had: how many parts used, or how many parts included a strategic material such as titanium. Written in XSB with SQL server as a backing store, and included some parsing, a bit of ontological reasoning and a little bit of NMR -- in parts using a WFS preference logic for parsing. Deductive Spread Sheet Implemented as add-in to MS Excel. Allows users to create deductive systems in a spreadsheet environment. XSB is backend computation engine and spreadsheet can be viewed as showing base data and the results of tabled computations. Whenever the user changes a spreadsheet cell that other cells depend on, those other cells are immediately updated. This is implemented using the new XSB incremental table maintenance facility. Found that bugs during program run and concomitant memory dumps could lead to disasters. Poor user interfaces cause users to believe the systems are also poor. Transfer of programs become difficult when users are unfamiliar with LP and they have to debug programs.
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NON-ACADEMIC APPLICATIONS -2
SPACE SHUTTLE REACTION CONTROL SYSTEM (GELFOND ET AL ) Primary responsibility - maneuver aircraft while in space. Consists of fuel and oxidizer tanks, valves and other plumbing needed to provide propellant to shuttle’s maneuvering jets. Includes electronic circuitry: both to control valves in fuel lines and to prepare jets to receive firing commands. During normal shuttle operations, pre-scripted plans tell astronauts what to do to achieve certain goals. System failures change situation. The number of possible sets of failures is too large to pre-plan for all of them. Continued correct operation of the RCS is then needed to allow mission completion of the mission and ensure crew safety. An intelligent system to verify and generate plans was needed. RCS/USA-Advisor is part of a decision support system for shuttle controllers. It is based on a reasoning system and a user interface. The reasoning system is capable of checking correctness of plans and finding plans for the operation of the RCS. Employs a programming methodology based on A-Prolog, algorithms for computing answer sets of programs of A-Prolog, and programming systems implementing these algorithms. User interface written in Java. Allows the user to specify the reasoning task to be performed, and then assembles into a program various A-Prolog modules, chosen according the components of the RCS that are involved in the task. Finally, the interface invokes program smodels to compute the answer sets of the A-Prolog program, and presents the results to the user.
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SPACE SHUTTLE (CON’T) Large Practical System written in A-Prolog
Importance of Careful Initial Design Simplified the Program Java Interface to Select Modules to Solve a Problem and Integrate Modules into Final A-Prolog Worked Well Structuring Problems as LP modules Useful for Reusability and Proving Correctness of Integration. System of Substantial Size Used for Planning Built on Theory of Action and Changes A-Prolog Allowed Use of Recursive Causal Laws System Tested and Worked. Not yet Used on a Space Mission. Demonstrates Practical Use of LPNMR Important to Collect and Publicize Successes in LPNMR
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LPNMR COMPANIES XSB, INC. (Warren, XSB) NEOTIDE (Simon, SMODELS)
Advanced Techniques To Transform Unstructured Data NEOTIDE (Simon, SMODELS) License SMODELS HERZUM (COLLABORATION with EXECURA –SPIN-OFF, CALABRIA, DLV) Market OLEX (Semantic Categorizer) and HiLeX Advanced Semantic Information Extractor Deductive Database efforts to market system were not successful. Lessons learned from them might be useful for these three companies. Should speak to two of the individuals most closely connected with it. If they are successful it will be a tremendous boon to our field.
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SUMMARY AND CONCLUSIONS
SIGNIFICANT DEVELOPMENTS/RELATIONSHIPS IN LPNMR LPNMR IS MATURE DISCIPLINE: THEORY/IMPLEMENTATIONS BASED ON LOGICAL FOUNDATIONS – NOT AD-HOCKERY SIGNIFICANT IMPLEMENTATIONS TOOLS AVAILABLE FOR REAL WORLD APPLICATIONS SEVERAL SYSTEMS SCALE TO LARGE PROBLEMS ADDITIONAL TOOLS NEEDED FOR USERS FUTURE DIRECTIONS ASP and Grounding – Extend to Variables Without Grounding SIGNIFICANT REALISTIC APPLICATION NEEDED EXPAND IMPLEMENTATION REPOSITORY EXPAND WORK TO LOGIC-BASED AI (and PROBABILISTIC METHODS) AGENTS AND BELIEFS, LOGIC AND LANGUAGE, MECHANICAL CHECKING, LOGIC FOR CAUSATION AND ACTIONS, COGNITIVE ROBOTICS, BIOLOGIC MODELS, … SEMANTIC WEB
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