From the Publisher:
Probabilistic Reasoning in Intelligent Systems is a complete andaccessible account of the theoretical foundations and computational methods that underlie plausible reasoning under uncertainty. The author provides a coherent explication of probability as a language for reasoning with partial belief and offers a unifying perspective on other AI approaches to uncertainty, such as the Dempster-Shafer formalism, truth maintenance systems, and nonmonotonic logic. The author distinguishes syntactic and semantic approaches to uncertaintyand offers techniques, based on belief networks, that provide a mechanism for making semantics-based systems operational. Specifically, network-propagation techniques serve as a mechanism for combining the theoretical coherence of probability theory with modern demands of reasoning-systems technology: modular declarative inputs, conceptually meaningful inferences, and parallel distributed computation. Application areas include diagnosis, forecasting, image interpretation, multi-sensor fusion, decision support systems, plan recognition, planning, speech recognitionin short, almost every task requiring that conclusions be drawn from uncertain clues and incomplete information.
Probabilistic Reasoning in Intelligent Systems will be of special interest to scholars and researchers in AI, decision theory, statistics, logic, philosophy, cognitive psychology, and the management sciences. Professionals in the areas of knowledge-based systems, operations research, engineering, and statistics will find theoretical and computational tools of immediate practical use. The book can also be used as an excellent text for graduate-level courses in AI, operations research, or applied probability.

Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference

SWRL : A semantic Web rule language combining OWL and ruleML

We propose a new family of description logics (DLs), called DL-Lite, specifically tailored to capture basic ontology languages, while keeping low complexity of reasoning. Reasoning here means not only computing subsumption between concepts and checking satisfiability of the whole knowledge base, but also answering complex queries (in particular, unions of conjunctive queries) over the instance level (ABox) of the DL knowledge base. We show that, for the DLs of the DL-Lite family, the usual DL reasoning tasks are polynomial in the size of the TBox, and query answering is LogSpace in the size of the ABox (i.e., in data complexity). To the best of our knowledge, this is the first result of polynomial-time data complexity for query answering over DL knowledge bases. Notably our logics allow for a separation between TBox and ABox reasoning during query evaluation: the part of the process requiring TBox reasoning is independent of the ABox, and the part of the process requiring access to the ABox can be carried out by an SQL engine, thus taking advantage of the query optimization strategies provided by current database management systems. Since even slight extensions to the logics of the DL-Lite family make query answering at least NLogSpace in data complexity, thus ruling out the possibility of using on-the-shelf relational technology for query processing, we can conclude that the logics of the DL-Lite family are the maximal DLs supporting efficient query answering over large amounts of instances.

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Tractable Reasoning and Efficient Query Answering in Description Logics: The DL-Lite Family

We present a model with leverage and margin constraints that vary across investors and time. We find evidence consistent with each of the model’s five central predictions: (1) Since constrained investors bid up high-beta assets, high beta is associated with low alpha, as we find empirically for U.S. equities, 20 international equity markets, Treasury bonds, corporate bonds, and futures; (2) A betting-against-beta (BAB) factor, which is long leveraged low beta assets and short high-beta assets, produces significant positive risk-adjusted returns; (3) When funding constraints tighten, the return of the BAB factor is low; (4) Increased funding liquidity risk compresses betas toward one; (5) More constrained investors hold riskier assets.

Betting Against Beta

An ontology is a formal description of concepts and relationships that can exist for a community of human and/or machine agents. The notion of ontologies is crucial for the purpose of enabling knowledge sharing and reuse. The Handbook on Ontologies provides a comprehensive overview of the current status and future prospectives of the field of ontologies considering ontology languages, ontology engineering methods, example ontologies, infrastructures and technologies for ontologies, and how to bring this all into ontology-based infrastructures and applications that are among the best of their kind. The field of ontologies has tremendously developed and grown in the five years since the first edition of the "Handbook on Ontologies". Therefore, its revision includes 21 completely new chapters as well as a major re-working of 15 chapters transferred to this second edition.

Handbook on Ontologies

We show how to interoperate, semantically and inferentially, between the leading Semantic Web approaches to rules (RuleML Logic Programs) and ontologies (OWL/DAML+OIL Description Logic) via analyzing their expressive intersection. To do so, we define a new intermediate knowledge representation (KR) contained within this intersection: Description Logic Programs (DLP), and the closely related Description Horn Logic (DHL) which is an expressive fragment of first-order logic (FOL). DLP provides a significant degree of expressiveness, substantially greater than the RDF-Schema fragment of Description Logic. We show how to perform DLP-fusion: the bidirectional translation of premises and inferences (including typical kinds of queries) from the DLP fragment of DL to LP, and vice versa from the DLP fragment of LP to DL. In particular, this translation enables one to "build rules on top of ontologies": it enables the rule KR to have access to DL ontological definitions for vocabulary primitives (e.g., predicates and individual constants) used by the rules. Conversely, the DLP-fusion technique likewise enables one to "build ontologies on top of rules": it enables ontological definitions to be supplemented by rules, or imported into DL from rules. It also enables available efficient LP inferencing algorithms/implementations to be exploited for reasoning over large-scale DL ontologies.

/pdf/description-logic-programs-combining-logic-programs-with-1wdrzmbuaz.pdf

Description logic programs: combining logic programs with description logic

/pdf/description-logic-programs-combining-logic-programs-with-24o31cord9.pdf

Description Logic Programs: Combining Logic Programs with Description Logic

Recent studies suggest that the conditional CAPM might hold, period-by-period, and that time-varying betas can explain the failures of the simple, unconditional CAPM. We argue, however, that variation in betas and expected returns would have to be implausibly large to explain important asset-pricing anomalies, like book-to-market and momentum. We test this conjecture by directly estimating conditional alphas and betas from short-window regressions, avoiding the need to specify conditioning information. The tests show, consistent with our analytical results, that the conditional CAPM performs nearly as poorly as the unconditional CAPM.

pdf/the-conditional-capm-does-not-explain-asset-pricing-34fewtfs49.pdf

The Conditional CAPM Does Not Explain Asset-Pricing Anomalies

We address why, and especially how, to represent business rules in e-commerce contracts. By contracts, we mean descriptions of goods and services offered or sought, including ancillary agreements detailing terms of a deal. We observe that rules are useful in contracts to represent conditional relationships, e.g., in terms & conditions, service provisions, and surrounding business processes, and we illustrate this point with several examples. We analyze requirements (desiderata) for representing such rules in contracts. The requirements include: declarative semantics so as to enable shared understanding and interoperability; prioritized conflict handling so as to enable modular updating/revision; ease of parsing; integration into WWW-world software engineering; direct executability; and computational tractability. We give a representational approach that consists of two novel aspects. First, we give a new fundamental knowledge representation formalism: a generalized version of Courteous Logic Programs (CLP), which expressively extends declarative ordinary logic programs (OLP) to include prioritized conflict handling, thus enabling modularity in specifying and revising rule-sets. Our approach to implementing CLP is a courteous compiler that transCopyright ACM. To appear: Proceedings of the 1st ACM Conference on Electronic Commerce (EC99), to be held Denver, Colorado, USA, Nov. 3–5, 1999. Edited by Michael P. Wellman. New York, NY: ACM Press, 1999. http://www.ibm.com/iac/ec99/ or http://www.acm.org. forms any CLP into a semantically equivalent OLP with moderate, tractable computational overhead. Second, we give a new XML encoding of CLP, called Business Rules Markup Language (BRML), suitable for interchange between heterogeneous commercial rule languages. BRML can also express a broad subset of ANSI-draft Knowledge Interchange Format (KIF) which overlaps with CLP. Our new approach, unlike previous approaches, provides not only declarative semantics but also prioritized conflict handling, ease of parsing, and integration into WWW-world software engineering. We argue that this new approach meets the overall requirements to a greater extent than any of the previous approaches, including than KIF, the leading previous declarative approach. We have implemented both aspects of our approach; a free alpha prototype called CommonRules was released on the Web in July of 1999, at http://alphaworks.ibm.com. An extended version of this paper will be available as a forthcoming IBM Research Report (at http://www.research.ibm.com).

/pdf/a-declarative-approach-to-business-rules-in-contracts-27s2ccjj8m.pdf

A Declarative Approach to Business Rules in Contracts: Courteous Logic Programs in XML

SweetDeal is a rule-based approach to representation of business contracts that enables software agents to create, evaluate, negotiate, and execute contracts with substantial automation and modularity. It builds upon the situated courteous logic programs knowledge representation in RuleML, the emerging standard for Semantic Web XML rules. Here, we newly extend the SweetDeal approach by also incorporating process knowledge descriptions whose ontologies are represented in DAML+OIL (emerging standard for Semantic Web ontologies) thereby enabling more complex contracts with behavioral provisions, especially for handling exception conditions (e.g., late delivery or non-payment) that might arise during the execution of the contract. This provides a foundation for representing and automating deals about services -- in particular, about Web Services, so as to help search, select, and compose them. Our system is also the first to combine emerging Semantic Web standards for knowledge representation of rules (RuleML) with ontologies (DAML+OIL) for a practical e-business application domain, and further to do so with process knowledge. This also newly fleshes out the evolving concept of Semantic Web Services. A prototype (soon public) is running.

/pdf/sweetdeal-representing-agent-contracts-with-exceptions-using-1m06w3uvl1.pdf

SweetDeal: representing agent contracts with exceptions using XML rules, ontologies, and process descriptions

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