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.
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
26247223
Combining Description Logic and Defeasible Logic for the Semantic Web
The importance of integrating rules and ontologies for the Semantic Web has been well addressed by many researchers. Defeasible Logic is a simple but efficient nonmonotonic language which can handle both defeasibility and priority. In this paper we propose a novel approach to combining Defeasible Logic with Description Logics by introducing the Description Defeasible Logic (DDL). DDL is similar to Defeasible Logic but it also contains queries to the Description Logic knowledge base. DDL allows nonmonotonic reasoning to be built on top of ontologies, and to a certain degree, allows ontologies to be built on top of nonmonotonic reasoning. We give some basic properties of DDL, one of which shows that DDL is a tractable language provided that the underlying Description Logic is tractable.
1885288
A Theory of Forgetting in Logic Programming
The study of forgetting for reasoning has attracted considerable attention in AI. However, much of the work on forgetting, and other related approaches such as independence, irrelevance and novelty, has been restricted to the classical logics. This paper describes a detailed theoretical investigation of the notion of forgetting in the context of logic programming. We first provide a semantic definition of forgetting under the answer sets for extended logic programs. We then discuss the desirable properties and some motivating examples. An important result of this study is an algorithm for computing the result of forgetting in a logic program. Furthermore, we present a modified version of the algorithm and show that the time complexity of the new algorithm is polynomial with respect to the size of the given logic program if the size of certain rules is fixed. We show how the proposed theory of forgetting can be used to characterize the logic program updates.
2316989
Observation-based Model for BDI-Agents
We present a new computational model of BDI-agents, called the observation-based BDI-model. The key point of this BDI-model is to express agents' beliefs, desires and intentions as a set of runs (computing paths), which is exactly a system in the interpreted system model, a well-known agent model due to Halpern and his colleagues. Our BDI-model is computationally grounded in that we are able to associate the BDI-agent model with a computer program, and formulas, involving agents' beliefs, desires (goals) and intentions, can be understood as properties of program computations. We present a sound and complete proof system with respect to our BDI-model and explore how symbolic model checking techniques can be applied to model checking BDI-agents. In order to make our BDI-model more flexible and practically realistic, we generalize it so that agents can have multiple sources of beliefs, goals and intentions.
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.
52857898
Schema Discovery for Semistructured Data
To formulate a meaningful query on semistructured data, such as on the Web, that matches some of the source's structure, we need first to discover something about how the information is represented in the source. This is referred to as schema discovery and was considered for a single object recently. In the case of multiple objects, the task of schema discovery is to identify typical structuring information of those objects as a whole. We motivate the schema discovery in this general setting and propose a framework and algorithm for it. We apply the framework to a real Web database, the Internet Movies Database, to discover typical schema of most voted movies.
11271251
Top-down specialization for information and privacy preservation
Releasing person-specific data in its most specific state poses a threat to individual privacy. This paper presents a practical and efficient algorithm for determining a generalized version of data that masks sensitive information and remains useful for modelling classification. The generalization of data is implemented by specializing or detailing the level of information in a top-down manner until a minimum privacy requirement is violated. This top-down specialization is natural and efficient for handling both categorical and continuous attributes. Our approach exploits the fact that data usually contains redundant structures for classification. While generalization may eliminate some structures, other structures emerge to help. Our results show that quality of classification can be preserved even for highly restrictive privacy requirements. This work has great applicability to both public and private sectors that share information for mutual benefits and productivity.
2174685
Privacy-enhancing personalized web search
Personalized web search is a promising way to improve search quality by customizing search results for people with individual information goals. However, users are uncomfortable with exposing private preference information to search engines. On the other hand, privacy is not absolute, and often can be compromised if there is a gain in service or profitability to the user. Thus, a balance must be struck between search quality and privacy protection. This paper presents a scalable way for users to automatically build rich user profiles. These profiles summarize a user.s interests into a hierarchical organization according to specific interests. Two parameters for specifying privacy requirements are proposed to help the user to choose the content and degree of detail of the profile information that is exposed to the search engine. Experiments showed that the user profile improved search quality when compared to standard MSN rankings. More importantly, results verified our hypothesis that a significant improvement on search quality can be achieved by only sharing some higher-level user profile information, which is potentially less sensitive than detailed personal information.
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.
6868150
FLIPS: Hybrid Adaptive Intrusion Prevention
Intrusion detection systems are fundamentally passive and fail–open. Because their primary task is classification, they do nothing to prevent an attack from succeeding. An intrusion prevention system (IPS) adds protection mechanisms that provide fail–safe semantics, automatic response capabilities, and adaptive enforcement. We present FLIPS (Feedback Learning IPS), a hybrid approach to host security that prevents binary code injection attacks. It incorporates three major components: an anomaly-based classifier, a signature-based filtering scheme, and a supervision framework that employs Instruction Set Randomization (ISR). Since ISR prevents code injection attacks and can also precisely identify the injected code, we can tune the classifier and the filter via a learning mechanism based on this feedback. Capturing the injected code allows FLIPS to construct signatures for zero-day exploits. The filter can discard input that is anomalous or matches known malicious input, effectively protecting the application from additional instances of an attack – even zero-day attacks or attacks that are metamorphic in nature. FLIPS does not require a known user base and can be deployed transparently to clients and with minimal impact on servers. We describe a prototype that protects HTTP servers, but FLIPS can be applied to a variety of server and client applications.