Applying Logic Programming to Knowledge Discovery on the Internet

LP (Logic Programming) has been successfully applied to knowledge discovery in many fields. The execution of the LP is based on the evaluation of the first order predicate. Usually the information involved in the predicates are local and homogenous, thus the evaluation process is relatively simple. However, the evaluation process become much more complicated when applied to KDD on the Internet where the information involved in the predicates maybe heterogeneous and distributed over many different sits. Therefor, we try to attack the problem in a multi agent system's framework so that the logic program can be written in a site independent style and deal easily with heterogeneous represented information.

A Limited Negative-based Three Value Semantics of Inductive Logic Programs

Inductive logic programming adopts the standard horn lope program as its logic framework for inductivelearning. Due to the fact, however, that the expressive power of horn logic is relatively limited and the mechansm ofnegation is mostly that of negation as failure, it is difficult to make full use of negative information and consequentlynot suitable for inductive learning. This Paper adopts nounal lope program as me language of inductive logic programsand presents accordingly a kind of semantics called Limited Negation semantics. The issues of direct denotation andinference of negation in concept induction are solved. The paper shows that LN is directly generalized for the semantics of Well-Founded in die significance Of optional negation and has superior theoretical features, especially the capability Of expressing and processing negation by introducing the constant ’false’. ExperimentS also show that the inductive concepts in learning are accurately interpreted with LN.

Incremental Computation of Success Patterns of Logic Programs

A method is presented for incrementally computing success patterns of logic programs. The set of success patterns of a logic program with respect to an abstraction is formulated as the success set of an equational logic program modulo an equality theory that is induced by the abstraction. The method is exemplified via depth and stump abstractions. Also presented are algorithms for computing most general unifiers modulo equality theories induced by depth and stump abstractions.

Fuzzy Logic Programming in Action with <i>FLOPER</i>

During the last years, we have developed the FLOPER platform for providing a practical support to the so-called Multi-Adjoint Logic Programming approach (MALP in brief), which represents an extremely flexible framework into the Fuzzy Logic Programming arena. Nowadays, FLOPER is useful for compiling (to standard Prolog code), executing and debugging (by drawing execution trees) MALP programs, and it is ready for being extended in the near future with powerful transformation and optimization techniques designed in our research group during the recent past. Our last update consists in the integration of a graphical interface for a comfortable interaction with the system which allows, among other capabilities, the use of projects for packing scripts and auxiliary definitions of fuzzy sets/connectives, together with fuzzy programs and their associated lattices modeling truth-degrees beyond the simpler crisp case ﹛true;false﹜.

A Framed Temporal Logic Programming Language

We discuss the projection temporal logic (PTL), based on a primitiveprojection operator, prj. A framing technique is also presented, using which a synchronizationoperator, await, is defined within the underlying logic. A framed temporal logic programminglanguage (FTLL) is presented. To illustrate how to use both the language and framing technique, someexamples are given.

Agent-Oriented Probabilistic Logic Programming

Currently, agent-based computing is an active research area, and great efforts have been made towards the agent-oriented programming both from a theoretical and practical view. However, most of them assume that there is no uncertainty in agents＇ mental state and their environment. In other words, under this assumption agent developers are just allowed to specify how his agent acts when the agent is 100% sure about what is true/false. In this paper, this unrealistic assumption is removed and a new agent-oriented probabilistic logic programming language is proposed, which can deal with uncertain information about the world. The programming language is based on a combination of features of probabilistic logic programming and imperative programming.

Comparison of Semantics of Disjunctive Logic Programs Based on Model-Equivalent Reduction

In this paper, it is shown that stable model semantics, perfect model semantics, and partial stable model semantics of disjunctive logic programs have the same expressive power with respect to the polynomial-time model-equivalent reduction. That is, taking perfect model semantics and stable model semantic as an example, any logic program P can be transformed in polynomial time to another logic program P＇ such that perfect models （resp. stable models） of P i-i correspond to stable models （resp. perfect models） of P＇, and the correspondence can be computed also in polynomial time. However, the minimal model semantics has weaker expressiveness than other mentioned semantics, otherwise, the polynomial hierarchy would collapse to NP.

A sequential model of bargaining in logic programming

This paper proposes a sequential model of bar- gaining specifying reasoning processes of an agent be- hind bargaining procedures. We encode agents＇ background knowledge, demands, and bargaining constraints in logic pro- grams and represent bargaining outcomes in answer sets. We assume that in each bargaining situation, each agent has a set of goals to achieve, which are normally unachievable with- out an agreement among all the agents who are involved in the bargaining. Through an alternating-offers procedure, an agreement among bargaining agents may be reached by ab- ductive reasoning. We show that the procedure converges to a Nash equilibrium if each agent makes rational offers/counter- offers in each round. In addition, the sequential model also has a number of desirable properties, such as mutual commit- ments, individual rationality, satisfactoriness, and honesty.

A characterization of answer sets for logic programs

Checking if a program has an answer set, and if so, compute its answer sets are just some of the important problems In answer set logic progremming. Solving these problems using Gelfond and Llfschltz＇s original definition of answer sets Is not an easy task. Alternative charaoterlzatlons of answer sets for nested logic programs by Erdem and Llfschltz, Lee and Llfschltz, and You at el. are based on the completion semantics and various notions of tlghtnese. However, the notion of tightness Is a local notion In the sense that for different answer sets there are, In general, different level mappings capturing their tlghtnese. This makes It hard to be used In the deelgn of algorithms for computing answer sets. This paper proposes a charecterization of answer sets based on sets of generetlng rules. From this charaoterlzation new algorithms are derived for computing answer sets and for performing some other reasoning teaks. As an application of the charecterlzatlon a sufficient and necessary condition for the equivalence between answer set sementics and completion semantics has been proven, and a basic theorem Is shown on computing answer sets for nested logic programs baaed on an extended notion of loop formulas. These results on tlghtnese and loop formulas are more general than that in You and Lin＇a work.

A New Integrated System of Logic Programming and Relational Database

Based on the study of the current two methods—interpretation and compilation—for the integration of logic programming and relational database,a new precompilation-based interpretive approach is proposed.It inherits the advantages of both methods,but overcomes the drawbacks of theirs.A new integrated system based on this approach is presented,which has been implemented on Micro VAX Ⅱ and applied to practise as the kernel of the GKBMS knowledge base management system.Also discussed are the key implementation techniques,including the coupling of logic and relational database systems,the compound of logic and relational database languages,the partial evaluation and static optimization of user's programs,fact scheduling and version management in problem-solving.

Complexities of Homomorphism and Isomorphism for Definite Logic Programs

A homomorphism φ of logic programs from P to P＇ is a function mapping Atoms（P） to Atoms（P＇） and it preserves complements and program clauses. For each definite program clause a ← a1,...,an ∈ P it implies that φ（a） ←- φ（a1）,...,φ（an） is a program clause of P＇. A homomorphism φis an isomorphism if φ is a bijection. In this paper, the complexity of the decision problems on homomorphism and isomorphism for definite logic programs is studied. It is shown that the homomorphism problem （HOM-LP） for definite logic programs is NP-complete, and the isomorphism problem （ISO-LP） is equivalent to the graph isomorphism problem （GI）.

An Implementation of Pure Horn Clause Logic Programming in a Reduction System

Many reduction systems have been presented for implementing functional programming languages. We propose here an extension of a reduction architecture to realize a kind of logic programming——pure Horn clause logic programming.This is an attempt to approach amalgama- tion of the two important programming paradigms.

On Local StratifiabUity of Logic Programs and Databases

In this paper,we deal with the problem of verifying local stratifiability of logic programs and databases presented by Przymusinski.Necessary and sufficient conditions for the local stratifiability of logic programs are presented and algorithms for performing the verification are developed.Finally,we prove that a database DB containing clauses with disjunctive consequents can be easily converted into a logic program P such that DB is locally stratified iff P is locally stratified.

Consistency Property of Finite FC-Normal Logic Programs

Marek＇s forward-chaining construction is one of the important techniques for investigating the non-monotonic reasoning. By introduction of consistency property over a logic program, they proposed a class of logic programs, FC-normal programs, each of which has at least one stable model. However, it is not clear how to choose one appropriate consistency property for deciding whether or not a logic program is FC-normal. In this paper, we firstly discover that, for any finite logic programⅡ, there exists the least consistency property LCon（Ⅱ） overⅡ, which just depends onⅡitself, such that, Ⅱ is FC-normal if and only ifⅡ is FC-normal with respect to （w.r.t.） LCon（Ⅱ）. Actually, in order to determine the FC-normality of a logic program, it is sufficient to check the monotonic closed sets in LCon（Ⅱ） for all non-monotonic rules, that is LFC（Ⅱ）. Secondly, we present an algorithm for computing LFC（Ⅱ）. Finally, we reveal that the brave reasoning task and cautious reasoning task for FC-normal logic programs are of the same difficulty as that of normal logic programs.

The Least Fixpoint Transformation for Disjunctive Logic Programs

The paradigm of disjunctive logic programming (DLP) enhances greatly the expressive power of normal logic programming (NLP) and many (declarative) semantics have beeu defined for DLP to cope with various problems of knowledge representation in artificial intelligence. However, the expressive ability of the semantics and the soundness of program transformations for DLP have been rarely explored. This paper defines an immediate consequence operator TGP for each disjunctive program and shows that TGP has the least and computable fixpoint Lft(P). Lft is, in fact, a program transformation for DLP which transforms all disjunctive programs into negative programs. It is shown that Lft preserves many key semantics, including the disjunctive stable models, well-founded model, disjunctive argument semantics DAS, three-valued models, etc. This means that every disjunctive program P has a unique canonical form Lft(P) with respect to these semanics. As a result, the work in this paper provides a unifying frameword for studying the expressive ability of various semantics for DLP.On the other hand, the computing of the above semantics for negative programs is just a trivial task, therefore, Lft(P) is also an optimization method for DLP. Another application of Lft is to derive some interesting semantic results for DLP.

MSVL： a typed language for temporal logic programming

The development of types is an important but challenging issue in temporal logic programming. In this paper, we investigate how to formalize and implement types in the temporal logic programming language MSVL, which is an executable subset of projection temporal logic （PTL）. Specifically, we extend MSVL with a few groups of types including basic data types, pointer types and struct types. On each type, we specify the domain of values and define some standard operations in terms of logic functions and predicates. Then, it is feasible to formalize statements of type declaration of program variables and statements of struct definitions as logic formulas. As the implementation of the theory, we extend the MSV toolkit with the support of modeling, simulation and verification of typed MSVL programs. Applications to the construction of AVL tree and ordered list show the practicality of the language.

On the Relationship Between TMS and Logic Programs

The relationship between TMS and general logic programs is an important issue in non-monotonic logic programming. In this paper, we prove that, after we translate the TMS theory into a general logic program, the TMS's well-founded assignment (orextension) is equivalent to the corresponding general logic program's stable model. It means that TMS can be completely integrated into a non-monotonic logic programming environment.

Symmetric Structure in Logic Programming

It is argued that some symmetric structure in logic programs could be taken into account when implementing semantics in logic programming. This may enhance the declarative ability or expressive power of the semantics. The work presented here may be seen as representative examples along this line. The focus is on the derivation of negative information and some other classic semantic issues. We first define a permutation group associated with a given logic program. Since usually the canonical models used to reflect the common sense or intended meaning are minimal or completed models of the program, we expose the relationships between minimal models and completed models of the original program and its so-called G-reduced form newlt-derived via the permutation group defined. By means of this G reduced form, we introduce a rule to assume negative information termed G-CWA, which is actually a generalization of the GCWA. We also develop the notions of G-definite, G-hierarchical and G-stratified logic programs, which are more general than definite, hierarchical and stratified programs, and extend some well-known declarative and procedural semantics to them, respectively. Keywords symmetry - logic programming - semantics Partially supported by the National Natural Science Foundation of China under Grant No.60373113.Jin-Zhao Wu was born in 1965. He obtained his Ph.D. degree in 1994 from the Institute of Systems Science, the Chinese Academy of Sciences. From 1994 to 1999 he was a post-doctoral and research scientist in Peking University and Max-Planck Institute of Computer Science. Since 2000 he has been working on the Faculty of Mathematics and Computer Science, University of Mannheim.Harald Fecher was born in 1972. He obtained his Ph.D. degree in 2003 from the Faculty of Mathematics and Computer Science, University of Mannheim. Since 2004 he has been research scientist in the Faculty of Computer Science, University of Kiel.

Identification of Tumor Evolution Patterns by Means of Inductive Logic Programming

In considering key events of genomic disorders in the development and progression of cancer, the correlation between genomic instability and carcinogenesis is currently under investigation. In this work, we propose an inductive logic programming approach to the problem of modeling evolution patterns for breast cancer. Using this approach, it is possible to extract fingerprints of stages of the disease that can be used in order to develop and deliver the most adequate therapies to patients. Furthermore, such a model can help physicians and biologists in the elucidation of molecular dynamics underlying the aberrations-waterfall model behind carcinogenesis. By showing results obtained some hints about further approach to the hypotheses. on a real-world dataset, we try to give knowledge-driven validations of such

IMPLEMENTATION OF 3-VALUED PARACONSISTENT LOGIC PROGRAMMING TOWARDS DECISION MAKING SYSTEM OF AGENTS

Due to the rapid development of applications of artificial intelligence and robotics in recent years, the necessity of reasoning and decision making with uncertain and inaccurate information is increasing. Since robots in the real world are always exposed to behavioral inaccuracies and uncertainty arising from recognition methods, they may occasionally encounter contradictory facts during reasoning on action decision. Paraconsistent logic programming is promising to make appropriate action decisions even when an agent is exposed to such uncertain information or contradictory facts, but there has been no implementation of this programming to the best of our knowledge. We propose a resolution algorithm for the 3-valued paraconsistent logic programming system QMPT0 and its implementation on SWI-Prolog. We also describe an application of the 3-valued paraconsistent logic programming regarding agent decision making.