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cs/0605055

Pierre Bessiere

David Bellot (INRIA Rh\^one-Alpes / Gravir-Imag), Pierre Bessiere (INRIA Rh\^one-Alpes / Gravir-Imag)

Approximate Discrete Probability Distribution Representation using a Multi-Resolution Binary Tree

International Conference on Tools for Artificial Intelligence - ITCAI 2003, Sacramento, (2003) --

cs.AI

Computing and storing probabilities is a hard problem as soon as one has to deal with complex distributions over multiple random variables. The problem of efficient representation of probability distributions is central in term of computational efficiency in the field of probabilistic reasoning. The main problem arises when dealing with joint probability distributions over a set of random variables: they are always represented using huge probability arrays. In this paper, a new method based on binary-tree representation is introduced in order to store efficiently very large joint distributions. Our approach approximates any multidimensional joint distributions using an adaptive discretization of the space. We make the assumption that the lower is the probability mass of a particular region of feature space, the larger is the discretization step. This assumption leads to a very optimized representation in term of time and memory. The other advantages of our approach are the ability to refine dynamically the distribution every time it is needed leading to a more accurate representation of the probability distribution and to an anytime representation of the distribution.

cs/0605108

Daowen Qiu

Fuchun Liu, Daowen Qiu, Hongyan Xing, and Zhujun Fan

Diagnosability of Fuzzy Discrete Event Systems

14 pages; revisions have been made

10.1007/978-3-540-74205-0_73

cs.AI

In order to more effectively cope with the real-world problems of vagueness, {\it fuzzy discrete event systems} (FDESs) were proposed recently, and the supervisory control theory of FDESs was developed. In view of the importance of failure diagnosis, in this paper, we present an approach of the failure diagnosis in the framework of FDESs. More specifically: (1) We formalize the definition of diagnosability for FDESs, in which the observable set and failure set of events are {\it fuzzy}, that is, each event has certain degree to be observable and unobservable, and, also, each event may possess different possibility of failure occurring. (2) Through the construction of observability-based diagnosers of FDESs, we investigate its some basic properties. In particular, we present a necessary and sufficient condition for diagnosability of FDESs. (3) Some examples serving to illuminate the applications of the diagnosability of FDESs are described. To conclude, some related issues are raised for further consideration.

cs/0605123

Jaime Cardoso

Jaime S. Cardoso

Classification of Ordinal Data

62 pages, MSc thesis

cs.AI

Classification of ordinal data is one of the most important tasks of relation learning. In this thesis a novel framework for ordered classes is proposed. The technique reduces the problem of classifying ordered classes to the standard two-class problem. The introduced method is then mapped into support vector machines and neural networks. Compared with a well-known approach using pairwise objects as training samples, the new algorithm has a reduced complexity and training time. A second novel model, the unimodal model, is also introduced and a parametric version is mapped into neural networks. Several case studies are presented to assert the validity of the proposed models.

cs/0606020

Vadim Astakhov

Vadim Astakhov, Tamara Astakhova, Brian Sanders

Imagination as Holographic Processor for Text Animation

10 pages, 10 figures, prototype presented at 4th International Conference on Computer Science and its Applications (ICCSA-2006), paper submited to SIGCHI 2007

cs.AI

Imagination is the critical point in developing of realistic artificial intelligence (AI) systems. One way to approach imagination would be simulation of its properties and operations. We developed two models: AI-Brain Network Hierarchy of Languages and Semantical Holographic Calculus as well as simulation system ScriptWriter that emulate the process of imagination through an automatic animation of English texts. The purpose of this paper is to demonstrate the model and to present ScriptWriter system http://nvo.sdsc.edu/NVO/JCSG/get_SRB_mime_file2.cgi//home/tamara.sdsc/test/demo.zip?F=/home/tamara.sdsc/test/demo.zip&M=application/x-gtar for simulation of the imagination.

cs/0606029

Audun Josang

Audun Josang

Belief Calculus

22 pages, 10 figures

cs.AI

In Dempster-Shafer belief theory, general beliefs are expressed as belief mass distribution functions over frames of discernment. In Subjective Logic beliefs are expressed as belief mass distribution functions over binary frames of discernment. Belief representations in Subjective Logic, which are called opinions, also contain a base rate parameter which express the a priori belief in the absence of evidence. Philosophically, beliefs are quantitative representations of evidence as perceived by humans or by other intelligent agents. The basic operators of classical probability calculus, such as addition and multiplication, can be applied to opinions, thereby making belief calculus practical. Through the equivalence between opinions and Beta probability density functions, this also provides a calculus for Beta probability density functions. This article explains the basic elements of belief calculus.

cs/0606066

Audun Josang

Audun Josang

The Cumulative Rule for Belief Fusion

cs.AI

The problem of combining beliefs in the Dempster-Shafer belief theory has attracted considerable attention over the last two decades. The classical Dempster's Rule has often been criticised, and many alternative rules for belief combination have been proposed in the literature. The consensus operator for combining beliefs has nice properties and produces more intuitive results than Dempster's rule, but has the limitation that it can only be applied to belief distribution functions on binary state spaces. In this paper we present a generalisation of the consensus operator that can be applied to Dirichlet belief functions on state spaces of arbitrary size. This rule, called the cumulative rule of belief combination, can be derived from classical statistical theory, and corresponds well with human intuition.

cs/0606081

Juergen Schmidhuber

Juergen Schmidhuber

New Millennium AI and the Convergence of History

Speed Prior: clarification / 15 pages, to appear in "Challenges to Computational Intelligence"

IDSIA-14-06

cs.AI

Artificial Intelligence (AI) has recently become a real formal science: the new millennium brought the first mathematically sound, asymptotically optimal, universal problem solvers, providing a new, rigorous foundation for the previously largely heuristic field of General AI and embedded agents. At the same time there has been rapid progress in practical methods for learning true sequence-processing programs, as opposed to traditional methods limited to stationary pattern association. Here we will briefly review some of the new results, and speculate about future developments, pointing out that the time intervals between the most notable events in over 40,000 years or 2^9 lifetimes of human history have sped up exponentially, apparently converging to zero within the next few decades. Or is this impression just a by-product of the way humans allocate memory space to past events?

cs/0607005

Florentin Smarandache

Florentin Smarandache, Jean Dezert

Belief Conditioning Rules (BCRs)

26 pages

cs.AI

In this paper we propose a new family of Belief Conditioning Rules (BCRs) for belief revision. These rules are not directly related with the fusion of several sources of evidence but with the revision of a belief assignment available at a given time according to the new truth (i.e. conditioning constraint) one has about the space of solutions of the problem.

cs/0607071

Peter J. Stuckey

H. Fang, Y. Kilani, J.H.M. Lee, and P.J. Stuckey

Islands for SAT

7 pages

cs.AI

In this note we introduce the notion of islands for restricting local search. We show how we can construct islands for CNF SAT problems, and how much search space can be eliminated by restricting search to the island.

cs/0607084

Farid Nouioua

Daniel Kayser (LIPN), Farid Nouioua (LIPN)

About Norms and Causes

The 17th FLAIRS'04 Conference (2004) 502-507

cs.AI

Knowing the norms of a domain is crucial, but there exist no repository of norms. We propose a method to extract them from texts: texts generally do not describe a norm, but rather how a state-of-affairs differs from it. Answers concerning the cause of the state-of-affairs described often reveal the implicit norm. We apply this idea to the domain of driving, and validate it by designing algorithms that identify, in a text, the "basic" norms to which it refers implicitly.

cs/0607086

Farid Nouioua

Daniel Kayser (LIPN), Farid Nouioua (LIPN)

Representing Knowledge about Norms

The 16th European Conference on Artificial Intelligence (ECAI'04) (2004) 363-367

cs.AI

Norms are essential to extend inference: inferences based on norms are far richer than those based on logical implications. In the recent decades, much effort has been devoted to reason on a domain, once its norms are represented. How to extract and express those norms has received far less attention. Extraction is difficult: as the readers are supposed to know them, the norms of a domain are seldom made explicit. For one thing, extracting norms requires a language to represent them, and this is the topic of this paper. We apply this language to represent norms in the domain of driving, and show that it is adequate to reason on the causes of accidents, as described by car-crash reports.

cs/0607143

Florentin Smarandache

Jean Dezert, Albena Tchamova, Florentin Smarandache, Pavlina Konstantinova

Target Type Tracking with PCR5 and Dempster's rules: A Comparative Analysis

10 pages, 5 diagrams. Presented to Fusion 2006 International Conference, Florence, Italy, July 2006

Proceedings of Fusion 2006 International Conference, Florence, Italy, July 2006

cs.AI

In this paper we consider and analyze the behavior of two combinational rules for temporal (sequential) attribute data fusion for target type estimation. Our comparative analysis is based on Dempster's fusion rule proposed in Dempster-Shafer Theory (DST) and on the Proportional Conflict Redistribution rule no. 5 (PCR5) recently proposed in Dezert-Smarandache Theory (DSmT). We show through very simple scenario and Monte-Carlo simulation, how PCR5 allows a very efficient Target Type Tracking and reduces drastically the latency delay for correct Target Type decision with respect to Demspter's rule. For cases presenting some short Target Type switches, Demspter's rule is proved to be unable to detect the switches and thus to track correctly the Target Type changes. The approach proposed here is totally new, efficient and promising to be incorporated in real-time Generalized Data Association - Multi Target Tracking systems (GDA-MTT) and provides an important result on the behavior of PCR5 with respect to Dempster's rule. The MatLab source code is provided in

cs/0607147

Florentin Smarandache

Florentin Smarandache, Jean Dezert

Fusion of qualitative beliefs using DSmT

13 pages. To appear in "Advances and Applications of DSmT for Information Fusion", collected works, second volume, 2006

Presented as an extended version (Tutorial MO2) to the Fusion 2006 International Conference, Florence, Italy, July 10-13, 2006

cs.AI

This paper introduces the notion of qualitative belief assignment to model beliefs of human experts expressed in natural language (with linguistic labels). We show how qualitative beliefs can be efficiently combined using an extension of Dezert-Smarandache Theory (DSmT) of plausible and paradoxical quantitative reasoning to qualitative reasoning. We propose a new arithmetic on linguistic labels which allows a direct extension of classical DSm fusion rule or DSm Hybrid rules. An approximate qualitative PCR5 rule is also proposed jointly with a Qualitative Average Operator. We also show how crisp or interval mappings can be used to deal indirectly with linguistic labels. A very simple example is provided to illustrate our qualitative fusion rules.

cs/0608002

Florentin Smarandache

Florentin Smarandache, Jean Dezert

An Introduction to the DSm Theory for the Combination of Paradoxical, Uncertain, and Imprecise Sources of Information

21 pages, many tables, figures. To appear in Information&Security International Journal, 2006

Presented at 13th International Congress of Cybernetics and Systems, Maribor, Slovenia, July 6-10, 2005.

cs.AI

The management and combination of uncertain, imprecise, fuzzy and even paradoxical or high conflicting sources of information has always been, and still remains today, of primal importance for the development of reliable modern information systems involving artificial reasoning. In this introduction, we present a survey of our recent theory of plausible and paradoxical reasoning, known as Dezert-Smarandache Theory (DSmT) in the literature, developed for dealing with imprecise, uncertain and paradoxical sources of information. We focus our presentation here rather on the foundations of DSmT, and on the two important new rules of combination, than on browsing specific applications of DSmT available in literature. Several simple examples are given throughout the presentation to show the efficiency and the generality of this new approach.

cs/0608019

Sebastian Brand

Sebastian Brand

Relation Variables in Qualitative Spatial Reasoning

14 pages; 27th German Conference on Artificial Intelligence (KI'04)

cs.AI

We study an alternative to the prevailing approach to modelling qualitative spatial reasoning (QSR) problems as constraint satisfaction problems. In the standard approach, a relation between objects is a constraint whereas in the alternative approach it is a variable. The relation-variable approach greatly simplifies integration and implementation of QSR. To substantiate this point, we discuss several QSR algorithms from the literature which in the relation-variable approach reduce to the customary constraint propagation algorithm enforcing generalised arc-consistency.

cs/0608028

Joseph Y. Halpern

Joseph Y. Halpern

Using Sets of Probability Measures to Represent Uncertainty

cs.AI

I explore the use of sets of probability measures as a representation of uncertainty.

cs/0609111

Tran Cao Son

Le-Chi Tuan, Chitta Baral, Tran Cao Son

A State-Based Regression Formulation for Domains with Sensing Actions<br> and Incomplete Information

34 pages, 7 Figures

Logical Methods in Computer Science, Volume 2, Issue 4 (October 2, 2006) lmcs:2238

10.2168/LMCS-2(4:2)2006

cs.AI

We present a state-based regression function for planning domains where an agent does not have complete information and may have sensing actions. We consider binary domains and employ a three-valued characterization of domains with sensing actions to define the regression function. We prove the soundness and completeness of our regression formulation with respect to the definition of progression. More specifically, we show that (i) a plan obtained through regression for a planning problem is indeed a progression solution of that planning problem, and that (ii) for each plan found through progression, using regression one obtains that plan or an equivalent one.

cs/0609132

Jochen Gruber

Jochen Gruber

Semantic Description of Parameters in Web Service Annotations

7 pages, 3 figures

cs.AI

A modification of OWL-S regarding parameter description is proposed. It is strictly based on Description Logic. In addition to class description of parameters it also allows the modelling of relations between parameters and the precise description of the size of data to be supplied to a service. In particular, it solves two major issues identified within current proposals for a Semantic Web Service annotation standard.

cs/0609136

Adeline Nazarenko

Adeline Nazarenko (LIPN), Erick Alphonse (LIPN), Julien Derivi\`ere (LIPN), Thierry Hamon (LIPN), Guillaume Vauvert (LIPN), Davy Weissenbacher (LIPN)

The ALVIS Format for Linguistically Annotated Documents

Proceedings of the fifth international conference on Language Resources and Evaluation, LREC 2006 (2006) 1782-1786

cs.AI

The paper describes the ALVIS annotation format designed for the indexing of large collections of documents in topic-specific search engines. This paper is exemplified on the biological domain and on MedLine abstracts, as developing a specialized search engine for biologists is one of the ALVIS case studies. The ALVIS principle for linguistic annotations is based on existing works and standard propositions. We made the choice of stand-off annotations rather than inserted mark-up. Annotations are encoded as XML elements which form the linguistic subsection of the document record.

cs/0609142

Bruno Scherrer

Bruno Scherrer (INRIA Lorraine - LORIA)

Modular self-organization

cs.AI

The aim of this paper is to provide a sound framework for addressing a difficult problem: the automatic construction of an autonomous agent's modular architecture. We combine results from two apparently uncorrelated domains: Autonomous planning through Markov Decision Processes and a General Data Clustering Approach using a kernel-like method. Our fundamental idea is that the former is a good framework for addressing autonomy whereas the latter allows to tackle self-organizing problems.

cs/0610006

Adrian Paschke

Adrian Paschke

A Typed Hybrid Description Logic Programming Language with Polymorphic Order-Sorted DL-Typed Unification for Semantic Web Type Systems

Full technical report 12/05. Published inn: Proc. of 2nd Int. Workshop on OWL: Experiences and Directions 2006 (OWLED'06) at ISWC'06, Athens, Georgia, USA, 2006

In: Proc. of 2nd Int. Workshop on OWL: Experiences and Directions 2006 (OWLED'06) at ISWC'06, Athens, Georgia, USA, 2006

cs.AI

In this paper we elaborate on a specific application in the context of hybrid description logic programs (hybrid DLPs), namely description logic Semantic Web type systems (DL-types) which are used for term typing of LP rules based on a polymorphic, order-sorted, hybrid DL-typed unification as procedural semantics of hybrid DLPs. Using Semantic Web ontologies as type systems facilitates interchange of domain-independent rules over domain boundaries via dynamically typing and mapping of explicitly defined type ontologies.

cs/0610015

Farid Nouioua

Farid Nouioua (LIPN)

Why did the accident happen? A norm-based reasoning approach

Logical Aspects of Computational Linguistics, student sessionUniversit\'{e} de bordeaux (Ed.) (2005) 31-34

cs.AI

In this paper we describe an architecture of a system that answer the question : Why did the accident happen? from the textual description of an accident. We present briefly the different parts of the architecture and then we describe with more detail the semantic part of the system i.e. the part in which the norm-based reasoning is performed on the explicit knowlege extracted from the text.

cs/0610023

Farid Nouioua

Farid Nouioua (LIPN), Daniel Kayser (LIPN)

Une exp\'{e}rience de s\'{e}mantique inf\'{e}rentielle

Actes de TALN'06UCL Presses Universitaires de Louvain (Ed.) (2006) 246-255

cs.AI

We develop a system which must be able to perform the same inferences that a human reader of an accident report can do and more particularly to determine the apparent causes of the accident. We describe the general framework in which we are situated, linguistic and semantic levels of the analysis and the inference rules used by the system.

cs/0610043

Zengyou He

Zengyou He

Farthest-Point Heuristic based Initialization Methods for K-Modes Clustering

7 pages

cs.AI

The k-modes algorithm has become a popular technique in solving categorical data clustering problems in different application domains. However, the algorithm requires random selection of initial points for the clusters. Different initial points often lead to considerable distinct clustering results. In this paper we present an experimental study on applying a farthest-point heuristic based initialization method to k-modes clustering to improve its performance. Experiments show that new initialization method leads to better clustering accuracy than random selection initialization method for k-modes clustering.

cs/0610060

Mark Levene

Mark Levene and Judit Bar-Ilan

Comparing Typical Opening Move Choices Made by Humans and Chess Engines

12 pages, 1 figure, 6 tables

cs.AI

The opening book is an important component of a chess engine, and thus computer chess programmers have been developing automated methods to improve the quality of their books. For chess, which has a very rich opening theory, large databases of high-quality games can be used as the basis of an opening book, from which statistics relating to move choices from given positions can be collected. In order to find out whether the opening books used by modern chess engines in machine versus machine competitions are ``comparable'' to those used by chess players in human versus human competitions, we carried out analysis on 26 test positions using statistics from two opening books one compiled from humans' games and the other from machines' games. Our analysis using several nonparametric measures, shows that, overall, there is a strong association between humans' and machines' choices of opening moves when using a book to guide their choices.

cs/0610111

Devavrat Shah

Kyomin Jung and Devavrat Shah

Local approximate inference algorithms

21 pages, 10 figures

cs.AI

We present a new local approximation algorithm for computing Maximum a Posteriori (MAP) and log-partition function for arbitrary exponential family distribution represented by a finite-valued pair-wise Markov random field (MRF), say $G$. Our algorithm is based on decomposition of $G$ into {\em appropriately} chosen small components; then computing estimates locally in each of these components and then producing a {\em good} global solution. We show that if the underlying graph $G$ either excludes some finite-sized graph as its minor (e.g. Planar graph) or has low doubling dimension (e.g. any graph with {\em geometry}), then our algorithm will produce solution for both questions within {\em arbitrary accuracy}. We present a message-passing implementation of our algorithm for MAP computation using self-avoiding walk of graph. In order to evaluate the computational cost of this implementation, we derive novel tight bounds on the size of self-avoiding walk tree for arbitrary graph. As a consequence of our algorithmic result, we show that the normalized log-partition function (also known as free-energy) for a class of {\em regular} MRFs will converge to a limit, that is computable to an arbitrary accuracy.

cs/0610140

Leonid Makarov

Leonid Makarov and Peter Komarov

Constant for associative patterns ensemble

6 pages

cs.AI

Creation procedure of associative patterns ensemble in terms of formal logic with using neural net-work (NN) model is formulated. It is shown that the associative patterns set is created by means of unique procedure of NN work which having individual parameters of entrance stimulus transformation. It is ascer-tained that the quantity of the selected associative patterns possesses is a constant.

cs/0610156

Fadi Badra

Mathieu D'Aquin (INRIA Lorraine - LORIA, KMI), Fadi Badra (INRIA Lorraine - LORIA), Sandrine Lafrogne (INRIA Lorraine - LORIA), Jean Lieber (INRIA Lorraine - LORIA), Amedeo Napoli (INRIA Lorraine - LORIA), Laszlo Szathmary (INRIA Lorraine - LORIA)

Adaptation Knowledge Discovery from a Case Base

Proceedings of the 17th European Conference on Artificial Intelligence (ECAI-06), Trento G. Brewka (Ed.) (2006) 795--796

cs.AI

In case-based reasoning, the adaptation step depends in general on domain-dependent knowledge, which motivates studies on adaptation knowledge acquisition (AKA). CABAMAKA is an AKA system based on principles of knowledge discovery from databases. This system explores the variations within the case base to elicit adaptation knowledge. It has been successfully tested in an application of case-based decision support to breast cancer treatment.

cs/0610165

Daowen Qiu

Fuchun Liu, Daowen Qiu, Hongyan Xing, and Zhujun Fan

Decentralized Failure Diagnosis of Stochastic Discrete Event Systems

25 pages. Comments and criticisms are welcome

IEEE Transactions on Automatic Control, 53 (2) (2008) 535-546.

cs.AI

Recently, the diagnosability of {\it stochastic discrete event systems} (SDESs) was investigated in the literature, and, the failure diagnosis considered was {\it centralized}. In this paper, we propose an approach to {\it decentralized} failure diagnosis of SDESs, where the stochastic system uses multiple local diagnosers to detect failures and each local diagnoser possesses its own information. In a way, the centralized failure diagnosis of SDESs can be viewed as a special case of the decentralized failure diagnosis presented in this paper with only one projection. The main contributions are as follows: (1) We formalize the notion of codiagnosability for stochastic automata, which means that a failure can be detected by at least one local stochastic diagnoser within a finite delay. (2) We construct a codiagnoser from a given stochastic automaton with multiple projections, and the codiagnoser associated with the local diagnosers is used to test codiagnosability condition of SDESs. (3) We deal with a number of basic properties of the codiagnoser. In particular, a necessary and sufficient condition for the codiagnosability of SDESs is presented. (4) We give a computing method in detail to check whether codiagnosability is violated. And (5) some examples are described to illustrate the applications of the codiagnosability and its computing method.

cs/0610175

Florentin Smarandache

Jean Dezert, Florentin Smarandache

DSmT: A new paradigm shift for information fusion

11 pages. Presented to Cogis06 International Conference, Paris, France, 2006

cs.AI

The management and combination of uncertain, imprecise, fuzzy and even paradoxical or high conflicting sources of information has always been and still remains of primal importance for the development of reliable information fusion systems. In this short survey paper, we present the theory of plausible and paradoxical reasoning, known as DSmT (Dezert-Smarandache Theory) in literature, developed for dealing with imprecise, uncertain and potentially highly conflicting sources of information. DSmT is a new paradigm shift for information fusion and recent publications have shown the interest and the potential ability of DSmT to solve fusion problems where Dempster's rule used in Dempster-Shafer Theory (DST) provides counter-intuitive results or fails to provide useful result at all. This paper is focused on the foundations of DSmT and on its main rules of combination (classic, hybrid and Proportional Conflict Redistribution rules). Shafer's model on which is based DST appears as a particular and specific case of DSm hybrid model which can be easily handled by DSmT as well. Several simple but illustrative examples are given throughout this paper to show the interest and the generality of this new theory.

cs/0611047

Adrian Paschke

Adrian Paschke

The Reaction RuleML Classification of the Event / Action / State Processing and Reasoning Space

The Reaction RuleML Classification of the Event / Action / State Processing and Reasoning Space extracted from Paschke, A.: ECA-RuleML: An Approach combining ECA Rules with temporal interval-based KR Event/Action Logics and Transactional Update Logics, Internet-based Information Systems, Technical University Munich, Technical Report 11 / 2005

Paschke, A.: The Reaction RuleML Classification of the Event / Action / State Processing and Reasoning Space, White Paper, October, 2006

cs.AI

Reaction RuleML is a general, practical, compact and user-friendly XML-serialized language for the family of reaction rules. In this white paper we give a review of the history of event / action /state processing and reaction rule approaches and systems in different domains, define basic concepts and give a classification of the event, action, state processing and reasoning space as well as a discussion of relevant / related work

cs/0611085

Timothy McJunkin

Timothy R. McJunkin and Jill R. Scott

Fuzzy Logic Classification of Imaging Laser Desorption Fourier Transform Mass Spectrometry Data

cs.AI

A fuzzy logic based classification engine has been developed for classifying mass spectra obtained with an imaging internal source Fourier transform mass spectrometer (I^2LD-FTMS). Traditionally, an operator uses the relative abundance of ions with specific mass-to-charge (m/z) ratios to categorize spectra. An operator does this by comparing the spectrum of m/z versus abundance of an unknown sample against a library of spectra from known samples. Automated positioning and acquisition allow I^2LD-FTMS to acquire data from very large grids, this would require classification of up to 3600 spectrum per hour to keep pace with the acquisition. The tedious job of classifying numerous spectra generated in an I^2LD-FTMS imaging application can be replaced by a fuzzy rule base if the cues an operator uses can be encapsulated. We present the translation of linguistic rules to a fuzzy classifier for mineral phases in basalt. This paper also describes a method for gathering statistics on ions, which are not currently used in the rule base, but which may be candidates for making the rule base more accurate and complete or to form new rule bases based on data obtained from known samples. A spatial method for classifying spectra with low membership values, based on neighboring sample classifications, is also presented.

cs/0611118

Florentin Smarandache

Haibin Wang, Andre Rogatko, Florentin Smarandache, Rajshekhar Sunderraman

A Neutrosophic Description Logic

18 pages. Presented at the IEEE International Conference on Granular Computing, Georgia State University, Atlanta, USA, May 2006

Proceedings of 2006 IEEE International Conference on Granular Computing, edited by Yan-Qing Zhang and Tsau Young Lin, Georgia State University, Atlanta, pp. 305-308, 2006

10.1142/S1793005708001100

cs.AI

Description Logics (DLs) are appropriate, widely used, logics for managing structured knowledge. They allow reasoning about individuals and concepts, i.e. set of individuals with common properties. Typically, DLs are limited to dealing with crisp, well defined concepts. That is, concepts for which the problem whether an individual is an instance of it is yes/no question. More often than not, the concepts encountered in the real world do not have a precisely defined criteria of membership: we may say that an individual is an instance of a concept only to a certain degree, depending on the individual's properties. The DLs that deal with such fuzzy concepts are called fuzzy DLs. In order to deal with fuzzy, incomplete, indeterminate and inconsistent concepts, we need to extend the fuzzy DLs, combining the neutrosophic logic with a classical DL. In particular, concepts become neutrosophic (here neutrosophic means fuzzy, incomplete, indeterminate, and inconsistent), thus reasoning about neutrosophic concepts is supported. We'll define its syntax, its semantics, and describe its properties.

cs/0611135

Marc Schoenauer

Christian Gagn\'e (INRIA Futurs, ISI), Marc Schoenauer (INRIA Futurs, LRI), Mich\`ele Sebag (LRI), Marco Tomassini (ISI)

Genetic Programming for Kernel-based Learning with Co-evolving Subsets Selection

Dans PPSN'06, 4193 (2006) 1008-1017

cs.AI

Support Vector Machines (SVMs) are well-established Machine Learning (ML) algorithms. They rely on the fact that i) linear learning can be formalized as a well-posed optimization problem; ii) non-linear learning can be brought into linear learning thanks to the kernel trick and the mapping of the initial search space onto a high dimensional feature space. The kernel is designed by the ML expert and it governs the efficiency of the SVM approach. In this paper, a new approach for the automatic design of kernels by Genetic Programming, called the Evolutionary Kernel Machine (EKM), is presented. EKM combines a well-founded fitness function inspired from the margin criterion, and a co-evolution framework ensuring the computational scalability of the approach. Empirical validation on standard ML benchmark demonstrates that EKM is competitive using state-of-the-art SVMs with tuned hyper-parameters.

cs/0611138

Marc Schoenauer

Vojtech Krmicek (INRIA Futurs, LRI), Mich\`ele Sebag (INRIA Futurs, LRI)

Functional Brain Imaging with Multi-Objective Multi-Modal Evolutionary Optimization

Dans PPSN'06, 4193 (2006) 382-391

cs.AI

Functional brain imaging is a source of spatio-temporal data mining problems. A new framework hybridizing multi-objective and multi-modal optimization is proposed to formalize these data mining problems, and addressed through Evolutionary Computation (EC). The merits of EC for spatio-temporal data mining are demonstrated as the approach facilitates the modelling of the experts' requirements, and flexibly accommodates their changing goals.

cs/0611141

Peter Tiedemann

Peter Tiedemann, Henrik Reif Andersen and Rasmus Pagh

A Generic Global Constraint based on MDDs

Tech report, 31 pages, 3 figures

cs.AI

The paper suggests the use of Multi-Valued Decision Diagrams (MDDs) as the supporting data structure for a generic global constraint. We give an algorithm for maintaining generalized arc consistency (GAC) on this constraint that amortizes the cost of the GAC computation over a root-to-terminal path in the search tree. The technique used is an extension of the GAC algorithm for the regular language constraint on finite length input. Our approach adds support for skipped variables, maintains the reduced property of the MDD dynamically and provides domain entailment detection. Finally we also show how to adapt the approach to constraint types that are closely related to MDDs, such as AOMDDs and Case DAGs.

cs/0612056

Gayathree U

U. Gayathree

Conscious Intelligent Systems - Part 1 : I X I

cs.AI

Did natural consciousness and intelligent systems arise out of a path that was co-evolutionary to evolution? Can we explain human self-consciousness as having risen out of such an evolutionary path? If so how could it have been? In this first part of a two-part paper (titled IXI), we take a learning system perspective to the problem of consciousness and intelligent systems, an approach that may look unseasonable in this age of fMRI's and high tech neuroscience. We posit conscious intelligent systems in natural environments and wonder how natural factors influence their design paths. Such a perspective allows us to explain seamlessly a variety of natural factors, factors ranging from the rise and presence of the human mind, man's sense of I, his self-consciousness and his looping thought processes to factors like reproduction, incubation, extinction, sleep, the richness of natural behavior, etc. It even allows us to speculate on a possible human evolution scenario and other natural phenomena.

cs/0612057

Gayathree U

U. Gayathree

Conscious Intelligent Systems - Part II - Mind, Thought, Language and Understanding

cs.AI

This is the second part of a paper on Conscious Intelligent Systems. We use the understanding gained in the first part (Conscious Intelligent Systems Part 1: IXI (arxiv id cs.AI/0612056)) to look at understanding. We see how the presence of mind affects understanding and intelligent systems; we see that the presence of mind necessitates language. The rise of language in turn has important effects on understanding. We discuss the humanoid question and how the question of self-consciousness (and by association mind/thought/language) would affect humanoids too.

cs/0612068

Esben Rune Hansen

Esben Rune Hansen and Henrik Reif Andersen

Interactive Configuration by Regular String Constraints

Tech Report

cs.AI

A product configurator which is complete, backtrack free and able to compute the valid domains at any state of the configuration can be constructed by building a Binary Decision Diagram (BDD). Despite the fact that the size of the BDD is exponential in the number of variables in the worst case, BDDs have proved to work very well in practice. Current BDD-based techniques can only handle interactive configuration with small finite domains. In this paper we extend the approach to handle string variables constrained by regular expressions. The user is allowed to change the strings by adding letters at the end of the string. We show how to make a data structure that can perform fast valid domain computations given some assignment on the set of string variables. We first show how to do this by using one large DFA. Since this approach is too space consuming to be of practical use, we construct a data structure that simulates the large DFA and in most practical cases are much more space efficient. As an example a configuration problem on $n$ string variables with only one solution in which each string variable is assigned to a value of length of $k$ the former structure will use $\Omega(k^n)$ space whereas the latter only need $O(kn)$. We also show how this framework easily can be combined with the recent BDD techniques to allow both boolean, integer and string variables in the configuration problem.

cs/0612109

Vicen G\'omez Cerd\`a

Vicenc Gomez, J. M. Mooij, H. J. Kappen

Truncating the loop series expansion for Belief Propagation

31 pages, 12 figures, submitted to Journal of Machine Learning Research

The Journal of Machine Learning Research, 8(Sep):1987--2016, 2007

cs.AI

Recently, M. Chertkov and V.Y. Chernyak derived an exact expression for the partition sum (normalization constant) corresponding to a graphical model, which is an expansion around the Belief Propagation solution. By adding correction terms to the BP free energy, one for each "generalized loop" in the factor graph, the exact partition sum is obtained. However, the usually enormous number of generalized loops generally prohibits summation over all correction terms. In this article we introduce Truncated Loop Series BP (TLSBP), a particular way of truncating the loop series of M. Chertkov and V.Y. Chernyak by considering generalized loops as compositions of simple loops. We analyze the performance of TLSBP in different scenarios, including the Ising model, regular random graphs and on Promedas, a large probabilistic medical diagnostic system. We show that TLSBP often improves upon the accuracy of the BP solution, at the expense of increased computation time. We also show that the performance of TLSBP strongly depends on the degree of interaction between the variables. For weak interactions, truncating the series leads to significant improvements, whereas for strong interactions it can be ineffective, even if a high number of terms is considered.

cs/0701013

Zengyou He

Zengyou He, Xaiofei Xu, Shengchun Deng

Attribute Value Weighting in K-Modes Clustering

15 pages

Tr-06-0615

cs.AI

In this paper, the traditional k-modes clustering algorithm is extended by weighting attribute value matches in dissimilarity computation. The use of attribute value weighting technique makes it possible to generate clusters with stronger intra-similarities, and therefore achieve better clustering performance. Experimental results on real life datasets show that these value weighting based k-modes algorithms are superior to the standard k-modes algorithm with respect to clustering accuracy.

cs/0701184

Joerg Hoffmann

Joerg Hoffmann and Carla Gomes and Bart Selman

Structure and Problem Hardness: Goal Asymmetry and DPLL Proofs in<br> SAT-Based Planning

Logical Methods in Computer Science, Volume 3, Issue 1 (February 26, 2007) lmcs:2228

10.2168/LMCS-3(1:6)2007

cs.AI

In Verification and in (optimal) AI Planning, a successful method is to formulate the application as boolean satisfiability (SAT), and solve it with state-of-the-art DPLL-based procedures. There is a lack of understanding of why this works so well. Focussing on the Planning context, we identify a form of problem structure concerned with the symmetrical or asymmetrical nature of the cost of achieving the individual planning goals. We quantify this sort of structure with a simple numeric parameter called AsymRatio, ranging between 0 and 1. We run experiments in 10 benchmark domains from the International Planning Competitions since 2000; we show that AsymRatio is a good indicator of SAT solver performance in 8 of these domains. We then examine carefully crafted synthetic planning domains that allow control of the amount of structure, and that are clean enough for a rigorous analysis of the combinatorial search space. The domains are parameterized by size, and by the amount of structure. The CNFs we examine are unsatisfiable, encoding one planning step less than the length of the optimal plan. We prove upper and lower bounds on the size of the best possible DPLL refutations, under different settings of the amount of structure, as a function of size. We also identify the best possible sets of branching variables (backdoors). With minimum AsymRatio, we prove exponential lower bounds, and identify minimal backdoors of size linear in the number of variables. With maximum AsymRatio, we identify logarithmic DPLL refutations (and backdoors), showing a doubly exponential gap between the two structural extreme cases. The reasons for this behavior -- the proof arguments -- illuminate the prototypical patterns of structure causing the empirical behavior observed in the competition benchmarks.

cs/0702028

Florentin Smarandache

Florentin Smarandache, Jean Dezert

Uniform and Partially Uniform Redistribution Rules

4 pages; "Advances and Applications of DSmT for Plausible and Paradoxical reasoning for Information Fusion", International Workshop organized by the Bulgarian IST Centre of Competence in 21st Century, December 14, 2006, Bulg. Acad. of Sciences, Sofia, Bulgaria

International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems (IJUFKS), World Scientific, Vol. 19, No. 6, 921-937, 2011

cs.AI

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

This short paper introduces two new fusion rules for combining quantitative basic belief assignments. These rules although very simple have not been proposed in literature so far and could serve as useful alternatives because of their low computation cost with respect to the recent advanced Proportional Conflict Redistribution rules developed in the DSmT framework.

cs/0702170

Peter Tiedemann

Peter Tiedemann, Henrik Reif Andersen, Rasmus Pagh

Generic Global Constraints based on MDDs

Preliminary 15 pages version of the tech-report cs.AI/0611141

cs.AI

Constraint Programming (CP) has been successfully applied to both constraint satisfaction and constraint optimization problems. A wide variety of specialized global constraints provide critical assistance in achieving a good model that can take advantage of the structure of the problem in the search for a solution. However, a key outstanding issue is the representation of 'ad-hoc' constraints that do not have an inherent combinatorial nature, and hence are not modeled well using narrowly specialized global constraints. We attempt to address this issue by considering a hybrid of search and compilation. Specifically we suggest the use of Reduced Ordered Multi-Valued Decision Diagrams (ROMDDs) as the supporting data structure for a generic global constraint. We give an algorithm for maintaining generalized arc consistency (GAC) on this constraint that amortizes the cost of the GAC computation over a root-to-leaf path in the search tree without requiring asymptotically more space than used for the MDD. Furthermore we present an approach for incrementally maintaining the reduced property of the MDD during the search, and show how this can be used for providing domain entailment detection. Finally we discuss how to apply our approach to other similar data structures such as AOMDDs and Case DAGs. The technique used can be seen as an extension of the GAC algorithm for the regular language constraint on finite length input.

cs/0703060

Florentin Smarandache

Jose L. Salmeron, Florentin Smarandache

Redesigning Decision Matrix Method with an indeterminacy-based inference process

12 pages, 4 figures, one table

A short version published in Advances in Fuzzy Sets and Systems, Vol. 1(2), 263-271, 2006

cs.AI

For academics and practitioners concerned with computers, business and mathematics, one central issue is supporting decision makers. In this paper, we propose a generalization of Decision Matrix Method (DMM), using Neutrosophic logic. It emerges as an alternative to the existing logics and it represents a mathematical model of uncertainty and indeterminacy. This paper proposes the Neutrosophic Decision Matrix Method as a more realistic tool for decision making. In addition, a de-neutrosophication process is included.

cs/0703124

Cheng-Yuan Liou

Cheng-Yuan Liou, Tai-Hei Wu, Chia-Ying Lee

Modelling Complexity in Musical Rhythm

21 pages, 13 figures, 2 tables

Complexity 15(4) (2010) 19~30 final form at http://www3.interscience.wiley.com/cgi-bin/fulltext/123191810/PDFSTART

cs.AI

This paper constructs a tree structure for the music rhythm using the L-system. It models the structure as an automata and derives its complexity. It also solves the complexity for the L-system. This complexity can resolve the similarity between trees. This complexity serves as a measure of psychological complexity for rhythms. It resolves the music complexity of various compositions including the Mozart effect K488. Keyword: music perception, psychological complexity, rhythm, L-system, automata, temporal associative memory, inverse problem, rewriting rule, bracketed string, tree similarity

cs/0703130

Robert Jeansoulin

Omar Doukari (LSIS), Robert Jeansoulin (IGM-LabInfo)

Space-contained conflict revision, for geographic information

14 pages

Proc. of 10th AGILE International Conference on Geographic Information Science, AGILE 2007. (07/05/2007) 1-14

cs.AI

Using qualitative reasoning with geographic information, contrarily, for instance, with robotics, looks not only fastidious (i.e.: encoding knowledge Propositional Logics PL), but appears to be computational complex, and not tractable at all, most of the time. However, knowledge fusion or revision, is a common operation performed when users merge several different data sets in a unique decision making process, without much support. Introducing logics would be a great improvement, and we propose in this paper, means for deciding -a priori- if one application can benefit from a complete revision, under only the assumption of a conjecture that we name the "containment conjecture", which limits the size of the minimal conflicts to revise. We demonstrate that this conjecture brings us the interesting computational property of performing a not-provable but global, revision, made of many local revisions, at a tractable size. We illustrate this approach on an application.

cs/0703156

Fadi Badra

Mathieu D'Aquin (KMI), Fadi Badra (INRIA Lorraine - LORIA), Sandrine Lafrogne (INRIA Lorraine - LORIA), Jean Lieber (INRIA Lorraine - LORIA), Amedeo Napoli (INRIA Lorraine - LORIA), Laszlo Szathmary (INRIA Lorraine - LORIA)

Case Base Mining for Adaptation Knowledge Acquisition

Dans Twentieth International Joint Conference on Artificial Intelligence - IJCAI'07 (2007) 750--755

cs.AI

In case-based reasoning, the adaptation of a source case in order to solve the target problem is at the same time crucial and difficult to implement. The reason for this difficulty is that, in general, adaptation strongly depends on domain-dependent knowledge. This fact motivates research on adaptation knowledge acquisition (AKA). This paper presents an approach to AKA based on the principles and techniques of knowledge discovery from databases and data-mining. It is implemented in CABAMAKA, a system that explores the variations within the case base to elicit adaptation knowledge. This system has been successfully tested in an application of case-based reasoning to decision support in the domain of breast cancer treatment.

cs/9308101

M. L. Ginsberg

Dynamic Backtracking

See http://www.jair.org/ for an online appendix and other files accompanying this article

Journal of Artificial Intelligence Research, Vol 1, (1993), 25-46

cs.AI

Because of their occasional need to return to shallow points in a search tree, existing backtracking methods can sometimes erase meaningful progress toward solving a search problem. In this paper, we present a method by which backtrack points can be moved deeper in the search space, thereby avoiding this difficulty. The technique developed is a variant of dependency-directed backtracking that uses only polynomial space while still providing useful control information and retaining the completeness guarantees provided by earlier approaches.

cs/9308102

M. P. Wellman

A Market-Oriented Programming Environment and its Application to Distributed Multicommodity Flow Problems

See http://www.jair.org/ for any accompanying files

Journal of Artificial Intelligence Research, Vol 1, (1993), 1-23

cs.AI

Market price systems constitute a well-understood class of mechanisms that under certain conditions provide effective decentralization of decision making with minimal communication overhead. In a market-oriented programming approach to distributed problem solving, we derive the activities and resource allocations for a set of computational agents by computing the competitive equilibrium of an artificial economy. WALRAS provides basic constructs for defining computational market structures, and protocols for deriving their corresponding price equilibria. In a particular realization of this approach for a form of multicommodity flow problem, we see that careful construction of the decision process according to economic principles can lead to efficient distributed resource allocation, and that the behavior of the system can be meaningfully analyzed in economic terms.

cs/9309101

I. P. Gent, T. Walsh

An Empirical Analysis of Search in GSAT

See http://www.jair.org/ for any accompanying files

Journal of Artificial Intelligence Research, Vol 1, (1993), 47-59

cs.AI

We describe an extensive study of search in GSAT, an approximation procedure for propositional satisfiability. GSAT performs greedy hill-climbing on the number of satisfied clauses in a truth assignment. Our experiments provide a more complete picture of GSAT's search than previous accounts. We describe in detail the two phases of search: rapid hill-climbing followed by a long plateau search. We demonstrate that when applied to randomly generated 3SAT problems, there is a very simple scaling with problem size for both the mean number of satisfied clauses and the mean branching rate. Our results allow us to make detailed numerical conjectures about the length of the hill-climbing phase, the average gradient of this phase, and to conjecture that both the average score and average branching rate decay exponentially during plateau search. We end by showing how these results can be used to direct future theoretical analysis. This work provides a case study of how computer experiments can be used to improve understanding of the theoretical properties of algorithms.

cs/9311101

F. Bergadano, D. Gunetti, U. Trinchero

The Difficulties of Learning Logic Programs with Cut

See http://www.jair.org/ for any accompanying files

Journal of Artificial Intelligence Research, Vol 1, (1993), 91-107

cs.AI

As real logic programmers normally use cut (!), an effective learning procedure for logic programs should be able to deal with it. Because the cut predicate has only a procedural meaning, clauses containing cut cannot be learned using an extensional evaluation method, as is done in most learning systems. On the other hand, searching a space of possible programs (instead of a space of independent clauses) is unfeasible. An alternative solution is to generate first a candidate base program which covers the positive examples, and then make it consistent by inserting cut where appropriate. The problem of learning programs with cut has not been investigated before and this seems to be a natural and reasonable approach. We generalize this scheme and investigate the difficulties that arise. Some of the major shortcomings are actually caused, in general, by the need for intensional evaluation. As a conclusion, the analysis of this paper suggests, on precise and technical grounds, that learning cut is difficult, and current induction techniques should probably be restricted to purely declarative logic languages.

cs/9311102

J. C. Schlimmer, L. A. Hermens

Software Agents: Completing Patterns and Constructing User Interfaces

See http://www.jair.org/ for an online appendix and other files accompanying this article

Journal of Artificial Intelligence Research, Vol 1, (1993), 61-89

cs.AI

To support the goal of allowing users to record and retrieve information, this paper describes an interactive note-taking system for pen-based computers with two distinctive features. First, it actively predicts what the user is going to write. Second, it automatically constructs a custom, button-box user interface on request. The system is an example of a learning-apprentice software- agent. A machine learning component characterizes the syntax and semantics of the user's information. A performance system uses this learned information to generate completion strings and construct a user interface. Description of Online Appendix: People like to record information. Doing this on paper is initially efficient, but lacks flexibility. Recording information on a computer is less efficient but more powerful. In our new note taking softwre, the user records information directly on a computer. Behind the interface, an agent acts for the user. To help, it provides defaults and constructs a custom user interface. The demonstration is a QuickTime movie of the note taking agent in action. The file is a binhexed self-extracting archive. Macintosh utilities for binhex are available from mac.archive.umich.edu. QuickTime is available from ftp.apple.com in the dts/mac/sys.soft/quicktime.

cs/9312101

M. Buchheit, F. M. Donini, A. Schaerf

Decidable Reasoning in Terminological Knowledge Representation Systems

See http://www.jair.org/ for any accompanying files

Journal of Artificial Intelligence Research, Vol 1, (1993), 109-138

cs.AI

Terminological knowledge representation systems (TKRSs) are tools for designing and using knowledge bases that make use of terminological languages (or concept languages). We analyze from a theoretical point of view a TKRS whose capabilities go beyond the ones of presently available TKRSs. The new features studied, often required in practical applications, can be summarized in three main points. First, we consider a highly expressive terminological language, called ALCNR, including general complements of concepts, number restrictions and role conjunction. Second, we allow to express inclusion statements between general concepts, and terminological cycles as a particular case. Third, we prove the decidability of a number of desirable TKRS-deduction services (like satisfiability, subsumption and instance checking) through a sound, complete and terminating calculus for reasoning in ALCNR-knowledge bases. Our calculus extends the general technique of constraint systems. As a byproduct of the proof, we get also the result that inclusion statements in ALCNR can be simulated by terminological cycles, if descriptive semantics is adopted.

cs/9401101

N. Nilsson

Teleo-Reactive Programs for Agent Control

See http://www.jair.org/ for any accompanying files

Journal of Artificial Intelligence Research, Vol 1, (1994), 139-158

cs.AI

A formalism is presented for computing and organizing actions for autonomous agents in dynamic environments. We introduce the notion of teleo-reactive (T-R) programs whose execution entails the construction of circuitry for the continuous computation of the parameters and conditions on which agent action is based. In addition to continuous feedback, T-R programs support parameter binding and recursion. A primary difference between T-R programs and many other circuit-based systems is that the circuitry of T-R programs is more compact; it is constructed at run time and thus does not have to anticipate all the contingencies that might arise over all possible runs. In addition, T-R programs are intuitive and easy to write and are written in a form that is compatible with automatic planning and learning methods. We briefly describe some experimental applications of T-R programs in the control of simulated and actual mobile robots.

cs/9402101

C. X. Ling

Learning the Past Tense of English Verbs: The Symbolic Pattern Associator vs. Connectionist Models

See http://www.jair.org/ for an online appendix and other files accompanying this article

Journal of Artificial Intelligence Research, Vol 1, (1994), 209-229

cs.AI

Learning the past tense of English verbs - a seemingly minor aspect of language acquisition - has generated heated debates since 1986, and has become a landmark task for testing the adequacy of cognitive modeling. Several artificial neural networks (ANNs) have been implemented, and a challenge for better symbolic models has been posed. In this paper, we present a general-purpose Symbolic Pattern Associator (SPA) based upon the decision-tree learning algorithm ID3. We conduct extensive head-to-head comparisons on the generalization ability between ANN models and the SPA under different representations. We conclude that the SPA generalizes the past tense of unseen verbs better than ANN models by a wide margin, and we offer insights as to why this should be the case. We also discuss a new default strategy for decision-tree learning algorithms.

cs/9402102

D. J. Cook, L. B. Holder

Substructure Discovery Using Minimum Description Length and Background Knowledge

See http://www.jair.org/ for an online appendix and other files accompanying this article

Journal of Artificial Intelligence Research, Vol 1, (1994), 231-255

cs.AI

The ability to identify interesting and repetitive substructures is an essential component to discovering knowledge in structural data. We describe a new version of our SUBDUE substructure discovery system based on the minimum description length principle. The SUBDUE system discovers substructures that compress the original data and represent structural concepts in the data. By replacing previously-discovered substructures in the data, multiple passes of SUBDUE produce a hierarchical description of the structural regularities in the data. SUBDUE uses a computationally-bounded inexact graph match that identifies similar, but not identical, instances of a substructure and finds an approximate measure of closeness of two substructures when under computational constraints. In addition to the minimum description length principle, other background knowledge can be used by SUBDUE to guide the search towards more appropriate substructures. Experiments in a variety of domains demonstrate SUBDUE's ability to find substructures capable of compressing the original data and to discover structural concepts important to the domain. Description of Online Appendix: This is a compressed tar file containing the SUBDUE discovery system, written in C. The program accepts as input databases represented in graph form, and will output discovered substructures with their corresponding value.

cs/9402103

M. Koppel, R. Feldman, A. M. Segre

Bias-Driven Revision of Logical Domain Theories

See http://www.jair.org/ for any accompanying files

Journal of Artificial Intelligence Research, Vol 1, (1994), 159-208

cs.AI

The theory revision problem is the problem of how best to go about revising a deficient domain theory using information contained in examples that expose inaccuracies. In this paper we present our approach to the theory revision problem for propositional domain theories. The approach described here, called PTR, uses probabilities associated with domain theory elements to numerically track the ``flow'' of proof through the theory. This allows us to measure the precise role of a clause or literal in allowing or preventing a (desired or undesired) derivation for a given example. This information is used to efficiently locate and repair flawed elements of the theory. PTR is proved to converge to a theory which correctly classifies all examples, and shown experimentally to be fast and accurate even for deep theories.

cs/9403101

P. M. Murphy, M. J. Pazzani

Exploring the Decision Forest: An Empirical Investigation of Occam's Razor in Decision Tree Induction

See http://www.jair.org/ for any accompanying files

Journal of Artificial Intelligence Research, Vol 1, (1994), 257-275

cs.AI

We report on a series of experiments in which all decision trees consistent with the training data are constructed. These experiments were run to gain an understanding of the properties of the set of consistent decision trees and the factors that affect the accuracy of individual trees. In particular, we investigated the relationship between the size of a decision tree consistent with some training data and the accuracy of the tree on test data. The experiments were performed on a massively parallel Maspar computer. The results of the experiments on several artificial and two real world problems indicate that, for many of the problems investigated, smaller consistent decision trees are on average less accurate than the average accuracy of slightly larger trees.

cs/9406101

A. Borgida, P. F. Patel-Schneider

A Semantics and Complete Algorithm for Subsumption in the CLASSIC Description Logic

See http://www.jair.org/ for any accompanying files

Journal of Artificial Intelligence Research, Vol 1, (1994), 277-308

cs.AI

This paper analyzes the correctness of the subsumption algorithm used in CLASSIC, a description logic-based knowledge representation system that is being used in practical applications. In order to deal efficiently with individuals in CLASSIC descriptions, the developers have had to use an algorithm that is incomplete with respect to the standard, model-theoretic semantics for description logics. We provide a variant semantics for descriptions with respect to which the current implementation is complete, and which can be independently motivated. The soundness and completeness of the polynomial-time subsumption algorithm is established using description graphs, which are an abstracted version of the implementation structures used in CLASSIC, and are of independent interest.

cs/9406102

R. Sebastiani

Applying GSAT to Non-Clausal Formulas

See http://www.jair.org/ for any accompanying files

Journal of Artificial Intelligence Research, Vol 1, (1994), 309-314

cs.AI

In this paper we describe how to modify GSAT so that it can be applied to non-clausal formulas. The idea is to use a particular ``score'' function which gives the number of clauses of the CNF conversion of a formula which are false under a given truth assignment. Its value is computed in linear time, without constructing the CNF conversion itself. The proposed methodology applies to most of the variants of GSAT proposed so far.

cs/9408101

A. J. Grove, J. Y. Halpern, D. Koller

Random Worlds and Maximum Entropy

See http://www.jair.org/ for any accompanying files

Journal of Artificial Intelligence Research, Vol 2, (1994), 33-88

cs.AI

Given a knowledge base KB containing first-order and statistical facts, we consider a principled method, called the random-worlds method, for computing a degree of belief that some formula Phi holds given KB. If we are reasoning about a world or system consisting of N individuals, then we can consider all possible worlds, or first-order models, with domain {1,...,N} that satisfy KB, and compute the fraction of them in which Phi is true. We define the degree of belief to be the asymptotic value of this fraction as N grows large. We show that when the vocabulary underlying Phi and KB uses constants and unary predicates only, we can naturally associate an entropy with each world. As N grows larger, there are many more worlds with higher entropy. Therefore, we can use a maximum-entropy computation to compute the degree of belief. This result is in a similar spirit to previous work in physics and artificial intelligence, but is far more general. Of equal interest to the result itself are the limitations on its scope. Most importantly, the restriction to unary predicates seems necessary. Although the random-worlds method makes sense in general, the connection to maximum entropy seems to disappear in the non-unary case. These observations suggest unexpected limitations to the applicability of maximum-entropy methods.

cs/9408102

T. Kitani, Y. Eriguchi, M. Hara

Pattern Matching and Discourse Processing in Information Extraction from Japanese Text

See http://www.jair.org/ for any accompanying files

Journal of Artificial Intelligence Research, Vol 2, (1994), 89-110

cs.AI

Information extraction is the task of automatically picking up information of interest from an unconstrained text. Information of interest is usually extracted in two steps. First, sentence level processing locates relevant pieces of information scattered throughout the text; second, discourse processing merges coreferential information to generate the output. In the first step, pieces of information are locally identified without recognizing any relationships among them. A key word search or simple pattern search can achieve this purpose. The second step requires deeper knowledge in order to understand relationships among separately identified pieces of information. Previous information extraction systems focused on the first step, partly because they were not required to link up each piece of information with other pieces. To link the extracted pieces of information and map them onto a structured output format, complex discourse processing is essential. This paper reports on a Japanese information extraction system that merges information using a pattern matcher and discourse processor. Evaluation results show a high level of system performance which approaches human performance.

cs/9408103

S. K. Murthy, S. Kasif, S. Salzberg

A System for Induction of Oblique Decision Trees

See http://www.jair.org/ for an online appendix and other files accompanying this article

Journal of Artificial Intelligence Research, Vol 2, (1994), 1-32

cs.AI

This article describes a new system for induction of oblique decision trees. This system, OC1, combines deterministic hill-climbing with two forms of randomization to find a good oblique split (in the form of a hyperplane) at each node of a decision tree. Oblique decision tree methods are tuned especially for domains in which the attributes are numeric, although they can be adapted to symbolic or mixed symbolic/numeric attributes. We present extensive empirical studies, using both real and artificial data, that analyze OC1's ability to construct oblique trees that are smaller and more accurate than their axis-parallel counterparts. We also examine the benefits of randomization for the construction of oblique decision trees.

cs/9409101

S. Safra, M. Tennenholtz

On Planning while Learning

See http://www.jair.org/ for any accompanying files

Journal of Artificial Intelligence Research, Vol 2, (1994), 111-129

cs.AI

This paper introduces a framework for Planning while Learning where an agent is given a goal to achieve in an environment whose behavior is only partially known to the agent. We discuss the tractability of various plan-design processes. We show that for a large natural class of Planning while Learning systems, a plan can be presented and verified in a reasonable time. However, coming up algorithmically with a plan, even for simple classes of systems is apparently intractable. We emphasize the role of off-line plan-design processes, and show that, in most natural cases, the verification (projection) part can be carried out in an efficient algorithmic manner.

cs/9412101

S. Soderland, Lehnert. W

Wrap-Up: a Trainable Discourse Module for Information Extraction

See http://www.jair.org/ for any accompanying files

Journal of Artificial Intelligence Research, Vol 2, (1994), 131-158

cs.AI

The vast amounts of on-line text now available have led to renewed interest in information extraction (IE) systems that analyze unrestricted text, producing a structured representation of selected information from the text. This paper presents a novel approach that uses machine learning to acquire knowledge for some of the higher level IE processing. Wrap-Up is a trainable IE discourse component that makes intersentential inferences and identifies logical relations among information extracted from the text. Previous corpus-based approaches were limited to lower level processing such as part-of-speech tagging, lexical disambiguation, and dictionary construction. Wrap-Up is fully trainable, and not only automatically decides what classifiers are needed, but even derives the feature set for each classifier automatically. Performance equals that of a partially trainable discourse module requiring manual customization for each domain.

cs/9412102

W. L. Buntine

Operations for Learning with Graphical Models

See http://www.jair.org/ for any accompanying files

Journal of Artificial Intelligence Research, Vol 2, (1994), 159-225

cs.AI

This paper is a multidisciplinary review of empirical, statistical learning from a graphical model perspective. Well-known examples of graphical models include Bayesian networks, directed graphs representing a Markov chain, and undirected networks representing a Markov field. These graphical models are extended to model data analysis and empirical learning using the notation of plates. Graphical operations for simplifying and manipulating a problem are provided including decomposition, differentiation, and the manipulation of probability models from the exponential family. Two standard algorithm schemas for learning are reviewed in a graphical framework: Gibbs sampling and the expectation maximization algorithm. Using these operations and schemas, some popular algorithms can be synthesized from their graphical specification. This includes versions of linear regression, techniques for feed-forward networks, and learning Gaussian and discrete Bayesian networks from data. The paper concludes by sketching some implications for data analysis and summarizing how some popular algorithms fall within the framework presented. The main original contributions here are the decomposition techniques and the demonstration that graphical models provide a framework for understanding and developing complex learning algorithms.

cs/9412103

S. Minton, J. Bresina, M. Drummond

Total-Order and Partial-Order Planning: A Comparative Analysis

See http://www.jair.org/ for an online appendix and other files accompanying this article

Journal of Artificial Intelligence Research, Vol 2, (1994), 227-262

cs.AI

For many years, the intuitions underlying partial-order planning were largely taken for granted. Only in the past few years has there been renewed interest in the fundamental principles underlying this paradigm. In this paper, we present a rigorous comparative analysis of partial-order and total-order planning by focusing on two specific planners that can be directly compared. We show that there are some subtle assumptions that underly the wide-spread intuitions regarding the supposed efficiency of partial-order planning. For instance, the superiority of partial-order planning can depend critically upon the search strategy and the structure of the search space. Understanding the underlying assumptions is crucial for constructing efficient planners.

cs/9501101

T. G. Dietterich, G. Bakiri

Solving Multiclass Learning Problems via Error-Correcting Output Codes

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Journal of Artificial Intelligence Research, Vol 2, (1995), 263-286

cs.AI

Multiclass learning problems involve finding a definition for an unknown function f(x) whose range is a discrete set containing k &gt 2 values (i.e., k ``classes''). The definition is acquired by studying collections of training examples of the form [x_i, f (x_i)]. Existing approaches to multiclass learning problems include direct application of multiclass algorithms such as the decision-tree algorithms C4.5 and CART, application of binary concept learning algorithms to learn individual binary functions for each of the k classes, and application of binary concept learning algorithms with distributed output representations. This paper compares these three approaches to a new technique in which error-correcting codes are employed as a distributed output representation. We show that these output representations improve the generalization performance of both C4.5 and backpropagation on a wide range of multiclass learning tasks. We also demonstrate that this approach is robust with respect to changes in the size of the training sample, the assignment of distributed representations to particular classes, and the application of overfitting avoidance techniques such as decision-tree pruning. Finally, we show that---like the other methods---the error-correcting code technique can provide reliable class probability estimates. Taken together, these results demonstrate that error-correcting output codes provide a general-purpose method for improving the performance of inductive learning programs on multiclass problems.

cs/9501102

S. Hanks, D. S. Weld

A Domain-Independent Algorithm for Plan Adaptation

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Journal of Artificial Intelligence Research, Vol 2, (1995), 319-360

cs.AI

The paradigms of transformational planning, case-based planning, and plan debugging all involve a process known as plan adaptation - modifying or repairing an old plan so it solves a new problem. In this paper we provide a domain-independent algorithm for plan adaptation, demonstrate that it is sound, complete, and systematic, and compare it to other adaptation algorithms in the literature. Our approach is based on a view of planning as searching a graph of partial plans. Generative planning starts at the graph's root and moves from node to node using plan-refinement operators. In planning by adaptation, a library plan - an arbitrary node in the plan graph - is the starting point for the search, and the plan-adaptation algorithm can apply both the same refinement operators available to a generative planner and can also retract constraints and steps from the plan. Our algorithm's completeness ensures that the adaptation algorithm will eventually search the entire graph and its systematicity ensures that it will do so without redundantly searching any parts of the graph.

cs/9501103

P. Cichosz

Truncating Temporal Differences: On the Efficient Implementation of TD(lambda) for Reinforcement Learning

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Journal of Artificial Intelligence Research, Vol 2, (1995), 287-318

cs.AI

Temporal difference (TD) methods constitute a class of methods for learning predictions in multi-step prediction problems, parameterized by a recency factor lambda. Currently the most important application of these methods is to temporal credit assignment in reinforcement learning. Well known reinforcement learning algorithms, such as AHC or Q-learning, may be viewed as instances of TD learning. This paper examines the issues of the efficient and general implementation of TD(lambda) for arbitrary lambda, for use with reinforcement learning algorithms optimizing the discounted sum of rewards. The traditional approach, based on eligibility traces, is argued to suffer from both inefficiency and lack of generality. The TTD (Truncated Temporal Differences) procedure is proposed as an alternative, that indeed only approximates TD(lambda), but requires very little computation per action and can be used with arbitrary function representation methods. The idea from which it is derived is fairly simple and not new, but probably unexplored so far. Encouraging experimental results are presented, suggesting that using lambda &gt 0 with the TTD procedure allows one to obtain a significant learning speedup at essentially the same cost as usual TD(0) learning.

cs/9503102

P. D. Turney

Cost-Sensitive Classification: Empirical Evaluation of a Hybrid Genetic Decision Tree Induction Algorithm

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Journal of Artificial Intelligence Research, Vol 2, (1995), 369-409

cs.AI

This paper introduces ICET, a new algorithm for cost-sensitive classification. ICET uses a genetic algorithm to evolve a population of biases for a decision tree induction algorithm. The fitness function of the genetic algorithm is the average cost of classification when using the decision tree, including both the costs of tests (features, measurements) and the costs of classification errors. ICET is compared here with three other algorithms for cost-sensitive classification - EG2, CS-ID3, and IDX - and also with C4.5, which classifies without regard to cost. The five algorithms are evaluated empirically on five real-world medical datasets. Three sets of experiments are performed. The first set examines the baseline performance of the five algorithms on the five datasets and establishes that ICET performs significantly better than its competitors. The second set tests the robustness of ICET under a variety of conditions and shows that ICET maintains its advantage. The third set looks at ICET's search in bias space and discovers a way to improve the search.

cs/9504101

S. K. Donoho, L. A. Rendell

Rerepresenting and Restructuring Domain Theories: A Constructive Induction Approach

See http://www.jair.org/ for an online appendix and other files accompanying this article

Journal of Artificial Intelligence Research, Vol 2, (1995), 411-446

cs.AI

Theory revision integrates inductive learning and background knowledge by combining training examples with a coarse domain theory to produce a more accurate theory. There are two challenges that theory revision and other theory-guided systems face. First, a representation language appropriate for the initial theory may be inappropriate for an improved theory. While the original representation may concisely express the initial theory, a more accurate theory forced to use that same representation may be bulky, cumbersome, and difficult to reach. Second, a theory structure suitable for a coarse domain theory may be insufficient for a fine-tuned theory. Systems that produce only small, local changes to a theory have limited value for accomplishing complex structural alterations that may be required. Consequently, advanced theory-guided learning systems require flexible representation and flexible structure. An analysis of various theory revision systems and theory-guided learning systems reveals specific strengths and weaknesses in terms of these two desired properties. Designed to capture the underlying qualities of each system, a new system uses theory-guided constructive induction. Experiments in three domains show improvement over previous theory-guided systems. This leads to a study of the behavior, limitations, and potential of theory-guided constructive induction.

cs/9505101

P. David

Using Pivot Consistency to Decompose and Solve Functional CSPs

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Journal of Artificial Intelligence Research, Vol 2, (1995), 447-474

cs.AI

Many studies have been carried out in order to increase the search efficiency of constraint satisfaction problems; among them, some make use of structural properties of the constraint network; others take into account semantic properties of the constraints, generally assuming that all the constraints possess the given property. In this paper, we propose a new decomposition method benefiting from both semantic properties of functional constraints (not bijective constraints) and structural properties of the network; furthermore, not all the constraints need to be functional. We show that under some conditions, the existence of solutions can be guaranteed. We first characterize a particular subset of the variables, which we name a root set. We then introduce pivot consistency, a new local consistency which is a weak form of path consistency and can be achieved in O(n^2d^2) complexity (instead of O(n^3d^3) for path consistency), and we present associated properties; in particular, we show that any consistent instantiation of the root set can be linearly extended to a solution, which leads to the presentation of the aforementioned new method for solving by decomposing functional CSPs.

cs/9505102

A. Schaerf, Y. Shoham, M. Tennenholtz

Adaptive Load Balancing: A Study in Multi-Agent Learning

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Journal of Artificial Intelligence Research, Vol 2, (1995), 475-500

cs.AI

We study the process of multi-agent reinforcement learning in the context of load balancing in a distributed system, without use of either central coordination or explicit communication. We first define a precise framework in which to study adaptive load balancing, important features of which are its stochastic nature and the purely local information available to individual agents. Given this framework, we show illuminating results on the interplay between basic adaptive behavior parameters and their effect on system efficiency. We then investigate the properties of adaptive load balancing in heterogeneous populations, and address the issue of exploration vs. exploitation in that context. Finally, we show that naive use of communication may not improve, and might even harm system efficiency.

cs/9505103

S. J. Russell, D. Subramanian

Provably Bounded-Optimal Agents

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Journal of Artificial Intelligence Research, Vol 2, (1995), 575-609

cs.AI

Since its inception, artificial intelligence has relied upon a theoretical foundation centered around perfect rationality as the desired property of intelligent systems. We argue, as others have done, that this foundation is inadequate because it imposes fundamentally unsatisfiable requirements. As a result, there has arisen a wide gap between theory and practice in AI, hindering progress in the field. We propose instead a property called bounded optimality. Roughly speaking, an agent is bounded-optimal if its program is a solution to the constrained optimization problem presented by its architecture and the task environment. We show how to construct agents with this property for a simple class of machine architectures in a broad class of real-time environments. We illustrate these results using a simple model of an automated mail sorting facility. We also define a weaker property, asymptotic bounded optimality (ABO), that generalizes the notion of optimality in classical complexity theory. We then construct universal ABO programs, i.e., programs that are ABO no matter what real-time constraints are applied. Universal ABO programs can be used as building blocks for more complex systems. We conclude with a discussion of the prospects for bounded optimality as a theoretical basis for AI, and relate it to similar trends in philosophy, economics, and game theory.

cs/9505104

W. W. Cohen

Pac-Learning Recursive Logic Programs: Efficient Algorithms

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Journal of Artificial Intelligence Research, Vol 2, (1995), 501-539

cs.AI

We present algorithms that learn certain classes of function-free recursive logic programs in polynomial time from equivalence queries. In particular, we show that a single k-ary recursive constant-depth determinate clause is learnable. Two-clause programs consisting of one learnable recursive clause and one constant-depth determinate non-recursive clause are also learnable, if an additional ``basecase'' oracle is assumed. These results immediately imply the pac-learnability of these classes. Although these classes of learnable recursive programs are very constrained, it is shown in a companion paper that they are maximally general, in that generalizing either class in any natural way leads to a computationally difficult learning problem. Thus, taken together with its companion paper, this paper establishes a boundary of efficient learnability for recursive logic programs.

cs/9505105

W. W. Cohen

Pac-learning Recursive Logic Programs: Negative Results

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Journal of Artificial Intelligence Research, Vol 2, (1995), 541-573

cs.AI

In a companion paper it was shown that the class of constant-depth determinate k-ary recursive clauses is efficiently learnable. In this paper we present negative results showing that any natural generalization of this class is hard to learn in Valiant's model of pac-learnability. In particular, we show that the following program classes are cryptographically hard to learn: programs with an unbounded number of constant-depth linear recursive clauses; programs with one constant-depth determinate clause containing an unbounded number of recursive calls; and programs with one linear recursive clause of constant locality. These results immediately imply the non-learnability of any more general class of programs. We also show that learning a constant-depth determinate program with either two linear recursive clauses or one linear recursive clause and one non-recursive clause is as hard as learning boolean DNF. Together with positive results from the companion paper, these negative results establish a boundary of efficient learnability for recursive function-free clauses.

cs/9506101

M. Veloso, P. Stone

FLECS: Planning with a Flexible Commitment Strategy

See http://www.jair.org/ for an online appendix and other files accompanying this article

Journal of Artificial Intelligence Research, Vol 3, (1995), 25-52

cs.AI

There has been evidence that least-commitment planners can efficiently handle planning problems that involve difficult goal interactions. This evidence has led to the common belief that delayed-commitment is the "best" possible planning strategy. However, we recently found evidence that eager-commitment planners can handle a variety of planning problems more efficiently, in particular those with difficult operator choices. Resigned to the futility of trying to find a universally successful planning strategy, we devised a planner that can be used to study which domains and problems are best for which planning strategies. In this article we introduce this new planning algorithm, FLECS, which uses a FLExible Commitment Strategy with respect to plan-step orderings. It is able to use any strategy from delayed-commitment to eager-commitment. The combination of delayed and eager operator-ordering commitments allows FLECS to take advantage of the benefits of explicitly using a simulated execution state and reasoning about planning constraints. FLECS can vary its commitment strategy across different problems and domains, and also during the course of a single planning problem. FLECS represents a novel contribution to planning in that it explicitly provides the choice of which commitment strategy to use while planning. FLECS provides a framework to investigate the mapping from planning domains and problems to efficient planning strategies.

cs/9506102

R. J. Mooney, M. E. Califf

Induction of First-Order Decision Lists: Results on Learning the Past Tense of English Verbs

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Journal of Artificial Intelligence Research, Vol 3, (1995), 1-24

cs.AI

This paper presents a method for inducing logic programs from examples that learns a new class of concepts called first-order decision lists, defined as ordered lists of clauses each ending in a cut. The method, called FOIDL, is based on FOIL (Quinlan, 1990) but employs intensional background knowledge and avoids the need for explicit negative examples. It is particularly useful for problems that involve rules with specific exceptions, such as learning the past-tense of English verbs, a task widely studied in the context of the symbolic/connectionist debate. FOIDL is able to learn concise, accurate programs for this problem from significantly fewer examples than previous methods (both connectionist and symbolic).

cs/9507101

R. Bergmann, W. Wilke

Building and Refining Abstract Planning Cases by Change of Representation Language

See http://www.jair.org/ for an online appendix and other files accompanying this article

Journal of Artificial Intelligence Research, Vol 3, (1995), 53-118

cs.AI

ion is one of the most promising approaches to improve the performance of problem solvers. In several domains abstraction by dropping sentences of a domain description -- as used in most hierarchical planners -- has proven useful. In this paper we present examples which illustrate significant drawbacks of abstraction by dropping sentences. To overcome these drawbacks, we propose a more general view of abstraction involving the change of representation language. We have developed a new abstraction methodology and a related sound and complete learning algorithm that allows the complete change of representation language of planning cases from concrete to abstract. However, to achieve a powerful change of the representation language, the abstract language itself as well as rules which describe admissible ways of abstracting states must be provided in the domain model. This new abstraction approach is the core of Paris (Plan Abstraction and Refinement in an Integrated System), a system in which abstract planning cases are automatically learned from given concrete cases. An empirical study in the domain of process planning in mechanical engineering shows significant advantages of the proposed reasoning from abstract cases over classical hierarchical planning.

cs/9508101

Q. Zhao, T. Nishida

Using Qualitative Hypotheses to Identify Inaccurate Data

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Journal of Artificial Intelligence Research, Vol 3, (1995), 119-145

cs.AI

Identifying inaccurate data has long been regarded as a significant and difficult problem in AI. In this paper, we present a new method for identifying inaccurate data on the basis of qualitative correlations among related data. First, we introduce the definitions of related data and qualitative correlations among related data. Then we put forward a new concept called support coefficient function (SCF). SCF can be used to extract, represent, and calculate qualitative correlations among related data within a dataset. We propose an approach to determining dynamic shift intervals of inaccurate data, and an approach to calculating possibility of identifying inaccurate data, respectively. Both of the approaches are based on SCF. Finally we present an algorithm for identifying inaccurate data by using qualitative correlations among related data as confirmatory or disconfirmatory evidence. We have developed a practical system for interpreting infrared spectra by applying the method, and have fully tested the system against several hundred real spectra. The experimental results show that the method is significantly better than the conventional methods used in many similar systems.

cs/9508102

C. G. Giraud-Carrier, T. R. Martinez

An Integrated Framework for Learning and Reasoning

See http://www.jair.org/ for an online appendix and other files accompanying this article

Journal of Artificial Intelligence Research, Vol 3, (1995), 147-185

cs.AI

Learning and reasoning are both aspects of what is considered to be intelligence. Their studies within AI have been separated historically, learning being the topic of machine learning and neural networks, and reasoning falling under classical (or symbolic) AI. However, learning and reasoning are in many ways interdependent. This paper discusses the nature of some of these interdependencies and proposes a general framework called FLARE, that combines inductive learning using prior knowledge together with reasoning in a propositional setting. Several examples that test the framework are presented, including classical induction, many important reasoning protocols and two simple expert systems.

cs/9510101

Y. Bengio, P. Frasconi

Diffusion of Context and Credit Information in Markovian Models

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Journal of Artificial Intelligence Research, Vol 3, (1995), 249-270

cs.AI

This paper studies the problem of ergodicity of transition probability matrices in Markovian models, such as hidden Markov models (HMMs), and how it makes very difficult the task of learning to represent long-term context for sequential data. This phenomenon hurts the forward propagation of long-term context information, as well as learning a hidden state representation to represent long-term context, which depends on propagating credit information backwards in time. Using results from Markov chain theory, we show that this problem of diffusion of context and credit is reduced when the transition probabilities approach 0 or 1, i.e., the transition probability matrices are sparse and the model essentially deterministic. The results found in this paper apply to learning approaches based on continuous optimization, such as gradient descent and the Baum-Welch algorithm.

cs/9510102

G. Pinkas, R. Dechter

Improving Connectionist Energy Minimization

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Journal of Artificial Intelligence Research, Vol 3, (1995), 223-248

cs.AI

Symmetric networks designed for energy minimization such as Boltzman machines and Hopfield nets are frequently investigated for use in optimization, constraint satisfaction and approximation of NP-hard problems. Nevertheless, finding a global solution (i.e., a global minimum for the energy function) is not guaranteed and even a local solution may take an exponential number of steps. We propose an improvement to the standard local activation function used for such networks. The improved algorithm guarantees that a global minimum is found in linear time for tree-like subnetworks. The algorithm, called activate, is uniform and does not assume that the network is tree-like. It can identify tree-like subnetworks even in cyclic topologies (arbitrary networks) and avoid local minima along these trees. For acyclic networks, the algorithm is guaranteed to converge to a global minimum from any initial state of the system (self-stabilization) and remains correct under various types of schedulers. On the negative side, we show that in the presence of cycles, no uniform algorithm exists that guarantees optimality even under a sequential asynchronous scheduler. An asynchronous scheduler can activate only one unit at a time while a synchronous scheduler can activate any number of units in a single time step. In addition, no uniform algorithm exists to optimize even acyclic networks when the scheduler is synchronous. Finally, we show how the algorithm can be improved using the cycle-cutset scheme. The general algorithm, called activate-with-cutset, improves over activate and has some performance guarantees that are related to the size of the network's cycle-cutset.

cs/9510103

K. Woods, D. Cook, L. Hall, K. Bowyer, L. Stark

Learning Membership Functions in a Function-Based Object Recognition System

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Journal of Artificial Intelligence Research, Vol 3, (1995), 187-222

cs.AI

Functionality-based recognition systems recognize objects at the category level by reasoning about how well the objects support the expected function. Such systems naturally associate a ``measure of goodness'' or ``membership value'' with a recognized object. This measure of goodness is the result of combining individual measures, or membership values, from potentially many primitive evaluations of different properties of the object's shape. A membership function is used to compute the membership value when evaluating a primitive of a particular physical property of an object. In previous versions of a recognition system known as Gruff, the membership function for each of the primitive evaluations was hand-crafted by the system designer. In this paper, we provide a learning component for the Gruff system, called Omlet, that automatically learns membership functions given a set of example objects labeled with their desired category measure. The learning algorithm is generally applicable to any problem in which low-level membership values are combined through an and-or tree structure to give a final overall membership value.

cs/9511101

S. B. Huffman, J. E. Laird

Flexibly Instructable Agents

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Journal of Artificial Intelligence Research, Vol 3, (1995), 271-324

cs.AI

This paper presents an approach to learning from situated, interactive tutorial instruction within an ongoing agent. Tutorial instruction is a flexible (and thus powerful) paradigm for teaching tasks because it allows an instructor to communicate whatever types of knowledge an agent might need in whatever situations might arise. To support this flexibility, however, the agent must be able to learn multiple kinds of knowledge from a broad range of instructional interactions. Our approach, called situated explanation, achieves such learning through a combination of analytic and inductive techniques. It combines a form of explanation-based learning that is situated for each instruction with a full suite of contextually guided responses to incomplete explanations. The approach is implemented in an agent called Instructo-Soar that learns hierarchies of new tasks and other domain knowledge from interactive natural language instructions. Instructo-Soar meets three key requirements of flexible instructability that distinguish it from previous systems: (1) it can take known or unknown commands at any instruction point; (2) it can handle instructions that apply to either its current situation or to a hypothetical situation specified in language (as in, for instance, conditional instructions); and (3) it can learn, from instructions, each class of knowledge it uses to perform tasks.

cs/9512101

G. I. Webb

OPUS: An Efficient Admissible Algorithm for Unordered Search

See http://www.jair.org/ for any accompanying files

Journal of Artificial Intelligence Research, Vol 3, (1995), 431-465

cs.AI

OPUS is a branch and bound search algorithm that enables efficient admissible search through spaces for which the order of search operator application is not significant. The algorithm's search efficiency is demonstrated with respect to very large machine learning search spaces. The use of admissible search is of potential value to the machine learning community as it means that the exact learning biases to be employed for complex learning tasks can be precisely specified and manipulated. OPUS also has potential for application in other areas of artificial intelligence, notably, truth maintenance.

cs/9512102

A. Broggi, S. Berte

Vision-Based Road Detection in Automotive Systems: A Real-Time Expectation-Driven Approach

See http://www.jair.org/ for any accompanying files

Journal of Artificial Intelligence Research, Vol 3, (1995), 325-348

cs.AI

The main aim of this work is the development of a vision-based road detection system fast enough to cope with the difficult real-time constraints imposed by moving vehicle applications. The hardware platform, a special-purpose massively parallel system, has been chosen to minimize system production and operational costs. This paper presents a novel approach to expectation-driven low-level image segmentation, which can be mapped naturally onto mesh-connected massively parallel SIMD architectures capable of handling hierarchical data structures. The input image is assumed to contain a distorted version of a given template; a multiresolution stretching process is used to reshape the original template in accordance with the acquired image content, minimizing a potential function. The distorted template is the process output.

cs/9512103

P. Idestam-Almquist

Generalization of Clauses under Implication

See http://www.jair.org/ for any accompanying files

Journal of Artificial Intelligence Research, Vol 3, (1995), 467-489

cs.AI

In the area of inductive learning, generalization is a main operation, and the usual definition of induction is based on logical implication. Recently there has been a rising interest in clausal representation of knowledge in machine learning. Almost all inductive learning systems that perform generalization of clauses use the relation theta-subsumption instead of implication. The main reason is that there is a well-known and simple technique to compute least general generalizations under theta-subsumption, but not under implication. However generalization under theta-subsumption is inappropriate for learning recursive clauses, which is a crucial problem since recursion is the basic program structure of logic programs. We note that implication between clauses is undecidable, and we therefore introduce a stronger form of implication, called T-implication, which is decidable between clauses. We show that for every finite set of clauses there exists a least general generalization under T-implication. We describe a technique to reduce generalizations under implication of a clause to generalizations under theta-subsumption of what we call an expansion of the original clause. Moreover we show that for every non-tautological clause there exists a T-complete expansion, which means that every generalization under T-implication of the clause is reduced to a generalization under theta-subsumption of the expansion.

cs/9512104

D. Heckerman, R. Shachter

Decision-Theoretic Foundations for Causal Reasoning

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Journal of Artificial Intelligence Research, Vol 3, (1995), 405-430

cs.AI

We present a definition of cause and effect in terms of decision-theoretic primitives and thereby provide a principled foundation for causal reasoning. Our definition departs from the traditional view of causation in that causal assertions may vary with the set of decisions available. We argue that this approach provides added clarity to the notion of cause. Also in this paper, we examine the encoding of causal relationships in directed acyclic graphs. We describe a special class of influence diagrams, those in canonical form, and show its relationship to Pearl's representation of cause and effect. Finally, we show how canonical form facilitates counterfactual reasoning.

cs/9512105

R. Khardon

Translating between Horn Representations and their Characteristic Models

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Journal of Artificial Intelligence Research, Vol 3, (1995), 349-372

cs.AI

Characteristic models are an alternative, model based, representation for Horn expressions. It has been shown that these two representations are incomparable and each has its advantages over the other. It is therefore natural to ask what is the cost of translating, back and forth, between these representations. Interestingly, the same translation questions arise in database theory, where it has applications to the design of relational databases. This paper studies the computational complexity of these problems. Our main result is that the two translation problems are equivalent under polynomial reductions, and that they are equivalent to the corresponding decision problem. Namely, translating is equivalent to deciding whether a given set of models is the set of characteristic models for a given Horn expression. We also relate these problems to the hypergraph transversal problem, a well known problem which is related to other applications in AI and for which no polynomial time algorithm is known. It is shown that in general our translation problems are at least as hard as the hypergraph transversal problem, and in a special case they are equivalent to it.

cs/9512106

M. Buro

Statistical Feature Combination for the Evaluation of Game Positions

See http://www.jair.org/ for any accompanying files

Journal of Artificial Intelligence Research, Vol 3, (1995), 373-382

cs.AI

This article describes an application of three well-known statistical methods in the field of game-tree search: using a large number of classified Othello positions, feature weights for evaluation functions with a game-phase-independent meaning are estimated by means of logistic regression, Fisher's linear discriminant, and the quadratic discriminant function for normally distributed features. Thereafter, the playing strengths are compared by means of tournaments between the resulting versions of a world-class Othello program. In this application, logistic regression - which is used here for the first time in the context of game playing - leads to better results than the other approaches.

cs/9512107

S. M. Weiss, N. Indurkhya

Rule-based Machine Learning Methods for Functional Prediction

See http://www.jair.org/ for any accompanying files

Journal of Artificial Intelligence Research, Vol 3, (1995), 383-403

cs.AI

We describe a machine learning method for predicting the value of a real-valued function, given the values of multiple input variables. The method induces solutions from samples in the form of ordered disjunctive normal form (DNF) decision rules. A central objective of the method and representation is the induction of compact, easily interpretable solutions. This rule-based decision model can be extended to search efficiently for similar cases prior to approximating function values. Experimental results on real-world data demonstrate that the new techniques are competitive with existing machine learning and statistical methods and can sometimes yield superior regression performance.

cs/9601101

P. vanBeek, D. W. Manchak

The Design and Experimental Analysis of Algorithms for Temporal Reasoning

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Journal of Artificial Intelligence Research, Vol 4, (1996), 1-18

cs.AI

Many applications -- from planning and scheduling to problems in molecular biology -- rely heavily on a temporal reasoning component. In this paper, we discuss the design and empirical analysis of algorithms for a temporal reasoning system based on Allen's influential interval-based framework for representing temporal information. At the core of the system are algorithms for determining whether the temporal information is consistent, and, if so, finding one or more scenarios that are consistent with the temporal information. Two important algorithms for these tasks are a path consistency algorithm and a backtracking algorithm. For the path consistency algorithm, we develop techniques that can result in up to a ten-fold speedup over an already highly optimized implementation. For the backtracking algorithm, we develop variable and value ordering heuristics that are shown empirically to dramatically improve the performance of the algorithm. As well, we show that a previously suggested reformulation of the backtracking search problem can reduce the time and space requirements of the backtracking search. Taken together, the techniques we develop allow a temporal reasoning component to solve problems that are of practical size.

cs/9602101

G. Brewka

Well-Founded Semantics for Extended Logic Programs with Dynamic Preferences

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Journal of Artificial Intelligence Research, Vol 4, (1996), 19-36

cs.AI

The paper describes an extension of well-founded semantics for logic programs with two types of negation. In this extension information about preferences between rules can be expressed in the logical language and derived dynamically. This is achieved by using a reserved predicate symbol and a naming technique. Conflicts among rules are resolved whenever possible on the basis of derived preference information. The well-founded conclusions of prioritized logic programs can be computed in polynomial time. A legal reasoning example illustrates the usefulness of the approach.

cs/9602102

A. L. Delcher, A. J. Grove, S. Kasif, J. Pearl

Logarithmic-Time Updates and Queries in Probabilistic Networks

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Journal of Artificial Intelligence Research, Vol 4, (1996), 37-59

cs.AI

Traditional databases commonly support efficient query and update procedures that operate in time which is sublinear in the size of the database. Our goal in this paper is to take a first step toward dynamic reasoning in probabilistic databases with comparable efficiency. We propose a dynamic data structure that supports efficient algorithms for updating and querying singly connected Bayesian networks. In the conventional algorithm, new evidence is absorbed in O(1) time and queries are processed in time O(N), where N is the size of the network. We propose an algorithm which, after a preprocessing phase, allows us to answer queries in time O(log N) at the expense of O(log N) time per evidence absorption. The usefulness of sub-linear processing time manifests itself in applications requiring (near) real-time response over large probabilistic databases. We briefly discuss a potential application of dynamic probabilistic reasoning in computational biology.

cs/9603101

T. Hogg

Quantum Computing and Phase Transitions in Combinatorial Search

See http://www.jair.org/ for an online appendix and other files accompanying this article

Journal of Artificial Intelligence Research, Vol 4, (1996), 91-128

cs.AI

We introduce an algorithm for combinatorial search on quantum computers that is capable of significantly concentrating amplitude into solutions for some NP search problems, on average. This is done by exploiting the same aspects of problem structure as used by classical backtrack methods to avoid unproductive search choices. This quantum algorithm is much more likely to find solutions than the simple direct use of quantum parallelism. Furthermore, empirical evaluation on small problems shows this quantum algorithm displays the same phase transition behavior, and at the same location, as seen in many previously studied classical search methods. Specifically, difficult problem instances are concentrated near the abrupt change from underconstrained to overconstrained problems.

cs/9603102

L. K. Saul, T. Jaakkola, M. I. Jordan

Mean Field Theory for Sigmoid Belief Networks

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Journal of Artificial Intelligence Research, Vol 4, (1996), 61-76

cs.AI

We develop a mean field theory for sigmoid belief networks based on ideas from statistical mechanics. Our mean field theory provides a tractable approximation to the true probability distribution in these networks; it also yields a lower bound on the likelihood of evidence. We demonstrate the utility of this framework on a benchmark problem in statistical pattern recognition---the classification of handwritten digits.

cs/9603103

J. R. Quinlan

Improved Use of Continuous Attributes in C4.5

See http://www.jair.org/ for any accompanying files

Journal of Artificial Intelligence Research, Vol 4, (1996), 77-90

cs.AI

A reported weakness of C4.5 in domains with continuous attributes is addressed by modifying the formation and evaluation of tests on continuous attributes. An MDL-inspired penalty is applied to such tests, eliminating some of them from consideration and altering the relative desirability of all tests. Empirical trials show that the modifications lead to smaller decision trees with higher predictive accuracies. Results also confirm that a new version of C4.5 incorporating these changes is superior to recent approaches that use global discretization and that construct small trees with multi-interval splits.