15783530
A Top-Down Procedure for Disjunctive Well-Founded Semantics
Skepticism is one of the most important semantic intuitions in artificial intelligence. The semantics formalizing skeptical reasoning in (disjunctive) logic programming is usually named well-founded semantics. However, the issue of defining and computing the well-founded semantics for disjunctive programs and databases has proved to be far more complex and difficult than for normal logic programs. The argumentation-based semantics WFDS is among the most promising proposals that attempts to define a natural well-founded semantics for disjunctive programs. In this paper, we propose a top-down procedure for WFDS called D-SLS Resolution, which naturally extends the Global SLS-resolution and SLI-resolution. We prove that D-SLS Resolution is sound and complete with respect to WFDS. This result in turn provides a further yet more powerful argument in favor of the WFDS.
8617754
Comparisons and computation of well-founded semantics for disjunctive logic programs
Much work has been done on extending the well-founded semantics to general disjunctive logic programs and various approaches have been proposed. However, these semantics are different from each other and no consensus is reached about which semantics is the most intended. In this article, we look at disjunctive well-founded reasoning from different angles. We show that there is an intuitive form of the well-founded reasoning in disjunctive logic programming which can be characterized by slightly modifying some existing approaches to defining disjunctive well-founded semantics, including program transformations, argumentation, unfounded sets (and resolution-like procedure). By employing the techniques developed by Brass and Dix in their transformation-based approach, we also provide a bottom-up procedure for this semantics. The significance of our work is not only in clarifying the relationship among different approaches, but also shed some light on what is an intended well-founded semantics for disjunctive logic programs.
4537262
A semantic framework for preference handling in answer set programming
We provide a semantic framework for preference handling in answer set programming. To this end, we introduce preference preserving consequence operators. The resulting fixpoint characterizations provide us with a uniform semantic framework for characterizing preference handling in existing approaches. Although our approach is extensible to other semantics by means of an alternating fixpoint theory, we focus here on the elaboration of preferences under answer set semantics. Alternatively, we show how these approaches can be characterized by the concept of order preservation. These uniform semantic characterizations provide us with new insights about inter-relationships and moreover about ways of implementation.
384033
Forgetting in Managing Rules and Ontologies
The language of HEX-programs under the answer-set semantics is designed for interoperating with heterogeneous sources via external atoms and for meta-reasoning via higher-order literals in the context of the semantic Web. As an important technique in managing knowledge bases, the notion of forgetting has received increasing interest in the knowledge-representation area. In this paper, we introduce a semantics-based theory of forgetting for HEX-programs and, in turn, for a class of OWL/RDF(S) ontologies which allows to fully employ semantic information in managing ontologies like editing, merging, aligning, and redundancy removal
14817828
Argumentation-based abduction in disjunctive logic programming
Abstract In this paper, we propose an argumentation-based semantic framework, called DAS, for disjunctive logic programming. The basic idea is to translate a disjunctive logic program into an argumentation-theoretic framework. One unique feature of our proposed framework is to consider the disjunctions of negative literals as possible assumptions so as to represent incomplete information. In our framework, three semantics preferred disjunctive hypothesis (PDH), complete disjunctive hypothesis (CDH) and well-founded disjunctive hypothesis (WFDH) are defined by three kinds of acceptable hypotheses to represent credulous, moderate and skeptical reasoning in artificial intelligence (AI), respectively. Furthermore, our semantic framework can be extended to a wider class than that of disjunctive programs (called bi-disjunctive logic programs). In addition to being a first serious attempt in establishing an argumentation-theoretic framework for disjunctive logic programming, DAS integrates and naturally extends many key semantics, such as the minimal models, extended generalized closed world assumption (EGCWA), the well-founded model, and the disjunctive stable models. In particular, novel and interesting argumentation-theoretic characterizations of the EGCWA and the disjunctive stable semantics are shown. Thus the framework presented in this paper does not only provide a new way of performing argumentation (abduction) in disjunctive deductive databases, but also is a simple, intuitive and unifying semantic framework for disjunctive logic programming.
2566357
Nested Epistemic Logic Programs
Nested logic programs and epistemic logic programs are two important extensions of answer set programming. However, the relationship between these two formalisms is rarely explored. In this paper we first introduce the epistemic HT-logic, and then propose a more general extension of logic programs called nested epistemic logic programs. The semantics of this extension – named equilibrium views – is defined on the basis of the epistemic HT-logic. We prove that equilibrium view semantics extends both the answer sets of nested logic programs and the world views of epistemic logic programs. Therefore, our work establishes a unifying framework for both nested logic programs and epistemic logic programs. Furthermore, we also provide a characterization of the strong equivalence of two nested epistemic logic programs.
36512646
A Peer-to-Peer Communication System
The demand of communicating anywhere, anytime on any device is growing with the development of modern information technology. Current instant messaging services enable people to be aware of each other's presence and to exchange text information. However, almost all of these services are based on client/server architecture. If servers crash, all connections are lost. As an alternative, peer-to-peer architecture basically relies on clients, but existing systems usually emphasize sharing of computing resources and files. In this paper, we propose a peer-to-peer communication system, which combines the advantages of instant messaging services and peer-to-peer networking. In the system, in addition to being aware of each other's presence, users can simultaneously attend multiple multimedia meetings with each meeting allowing multiple attendees. Users have full control over the exchange of real-time text/voice/video information in these meetings.
12496214
Anomaly Detection in Computer Security and an Application to File System Accesses
We present an overview of anomaly detection used in computer security, and provide a detailed example of a host-based Intrusion Detection System that monitors file systems to detect abnormal accesses. The File Wrapper Anomaly Detector (FWRAP) has two parts, a sensor that audits file systems, and an unsupervised machine learning system that computes normal models of those accesses. FWRAP employs the Probabilistic Anomaly Detection (PAD) algorithm previously reported in our work on Windows Registry Anomaly Detection. FWRAP represents a general approach to anomaly detection. The detector is first trained by operating the host computer for some amount of time and a model specific to the target machine is automatically computed by PAD. The model is then deployed to a real-time detector. In this paper we describe the feature set used to model file system accesses, and the performance results of a set of experiments using the sensor while attacking a Linux host with a variety of malware exploits. The PAD detector achieved impressive detection rates in some cases over 95% and about a 2% false positive rate when alarming on anomalous processes.
52857819
QUICK: graphical user interface to multiple databases
Formulating queries to access multiple databases can be a formidable task, especially when many terms from various databases and complex constraints are involved. To specify a multidatabase query, the user usually has to search through documents for exact database terms and learn the multidatabase language. The report presents QUICK (QUery Interface to CPL-Kleisli), a graphical user interface to multiple databases, CPL is a high-level multidatabase language built on top of an open query system Kleisli. QUICK allows users to handle overwhelming information from different data sources in an intuitive and uniform manner. The query specification is reduced to specifying user's terms in his/her own world, selecting paths and specifying constraints in a graph. QUICK is able to automatically generate a CPL query that corresponds to the user's intent.
304685
Anonymizing sequential releases
An organization makes a new release as new information become available, releases a tailored view for each data request, releases sensitive information and identifying information separately. The availability of related releases sharpens the identification of individuals by a global quasi-identifier consisting of attributes from related releases. Since it is not an option to anonymize previously released data, the current release must be anonymized to ensure that a global quasi-identifier is not effective for identification. In this paper, we study the sequential anonymization problem under this assumption. A key question is how to anonymize the current release so that it cannot be linked to previous releases yet remains useful for its own release purpose. We introduce the lossy join, a negative property in relational database design, as a way to hide the join relationship among releases, and propose a scalable and practical solution.
1098870
Hierarchical Document Clustering using Frequent Itemsets
A major challenge in document clustering is the extremely high dimensionality. For example, the vocabulary for a document set can easily be thousands of words. On the other hand, each document often contains a small fraction of words in the vocabulary. These features require special handlings. Another requirement is hierarchical clustering where clustered documents can be browsed according to the increasing specificity of topics. In this paper, we propose to use the notion of frequent itemsets, which comes from association rule mining, for document clustering. The intuition of our clustering criterion is that each cluster is identified by some common words, called frequent itemsets, for the documents in the cluster. Frequent itemsets are also used to produce a hierarchical topic tree for clusters. By focusing on frequent items, the dimensionality of the document set is drastically reduced. We show that this method outperforms best existing methods in terms of both clustering accuracy and scalability.