An Integrated Framework for Learning and Reasoning

Journal of Artificial Intelligence Research

Dep artment of Computer Scienc e, Brigham Y oung University Pr ovo, UT 84602 U.S.A. Abstract Learning and reasoning are b oth asp ects of what is considered to b e in telligence. Ho w-ev er, learning and reasoning are in man yw a ys in terdep enden t. This pap er discusses the nature of some of these in terdep endencies and prop oses a general framew ork called FLARE, that com bines inductiv e learning using prior kno wledge together with reasoning in a prop o-sitional setting. Sev eral examples that test the framew ork are presen ted, including classical induction, man y imp ortan t reasoning proto cols and t w o simple exp ert systems. Deduction is a form of reasoning with and ab out acquired kno wledge. It t ypically do es not result in the generation of new facts, rather it establishes cause-e ect relationships b et w een existing facts. Deduction ma y b e applied forwar d b y seeking the consequences of certain existing h yp otheses or b ackwar d to disco v er the necessary ...


Decidable Reasoning in Terminological Knowledge Representation Systems

Journal of Artificial Intelligence Research

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.


Unifying Class-Based Representation Formalisms

Journal of Artificial Intelligence Research

The notion of class is ubiquitous in computer science and is central in many formalisms for the representation of structured knowledge used both in knowledge representation and in databases. In this paper we study the basic issues underlying such representation formalisms and single out both their common characteristics and their distinguishing features. Such investigation leads us to propose a unifying framework in which we are able to capture the fundamental aspects of several representation languages used in different contexts. The proposed formalism is expressed in the style of description logics, which have been introduced in knowledge representation as a means to provide a semantically well-founded basis for the structural aspects of knowledge representation systems. The description logic considered in this paper is a subset of first order logic with nice computational characteristics. It is quite expressive and features a novel combination of constructs that has not been studied before. The distinguishing constructs are number restrictions, which generalize existence and functional dependencies, inverse roles, which allow one to refer to the inverse of a relationship, and possibly cyclic assertions, which are necessary for capturing real world domains. We are able to show that it is precisely such combination of constructs that makes our logic powerful enough to model the essential set of features for defining class structures that are common to frame systems, object-oriented database languages, and semantic data models. As a consequence of the established correspondences, several significant extensions of each of the above formalisms become available. The high expressiveness of the logic we propose and the need for capturing the reasoning in different contexts forces us to distinguish between unrestricted and finite model reasoning. A notable feature of our proposal is that reasoning in both cases is decidable. We argue that, by virtue of the high expressive power and of the associated reasoning capabilities on both unrestricted and finite models, our logic provides a common core for class-based representation formalisms.


Substructure Discovery Using Minimum Description Length and Background Knowledge

Journal of Artificial Intelligence Research

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.


Statistical Analysis of Peer Detailing for Children’s Diarrhea Treatments

AAAI Conferences

Diarrhea is one of the leading killers of children under five years old in Nigeria. To tackle this challenge, many activities were conducted by government institutions, non-governmental organizations, and private companies to promote the usage of the best treatment for the disease: the combination of Zinc and Oral Rehydration Salts (ORS). One of the activities, undertaken by the Clinton Health Access Initiative (CHAI), is hiring and training peer detailers to explain the benefits of the treatment to patent and proprietary medicine vendors (PPMVs), a major source of primary healthcare in Nigeria. This paper investigates the effectiveness of this information dissemination program for treatments of children’s diarrhea in Nigeria. Two aspects are considered when evaluating the effectiveness of the program: awareness (knowledge) and availability (inventory) of Zinc and ORS among PPMVs. We performed exploratory data analysis and statistical hypothesis tests, and found that the percentage of PPMVs with the desired knowledge of treatments for children’s diarrhea increases significantly in most states in Nigeria after the peer detailing. On the other hand, no significant patterns are detected for the percentage of PPMVs with inventory of the treatments. Logistic regression models with confounding factors suggest that PPMVs promoted by CHAI have significantly higher ratios of both the knowledge and inventory of the best treatment for children’s diarrhea.