Matching Items (9)
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- All Subjects: Database management
- All Subjects: Logic programming
- Genre: Academic theses
- Genre: Doctoral Dissertation
- Creators: Liu, Huan
- Creators: Lifschitz, Vladimir
Description
Answer Set Programming (ASP) is one of the most prominent and successful knowledge representation paradigms. The success of ASP is due to its expressive non-monotonic modeling language and its efficient computational methods originating from building propositional satisfiability solvers. The wide adoption of ASP has motivated several extensions to its modeling language in order to enhance expressivity, such as incorporating aggregates and interfaces with ontologies. Also, in order to overcome the grounding bottleneck of computation in ASP, there are increasing interests in integrating ASP with other computing paradigms, such as Constraint Programming (CP) and Satisfiability Modulo Theories (SMT). Due to the non-monotonic nature of the ASP semantics, such enhancements turned out to be non-trivial and the existing extensions are not fully satisfactory. We observe that one main reason for the difficulties rooted in the propositional semantics of ASP, which is limited in handling first-order constructs (such as aggregates and ontologies) and functions (such as constraint variables in CP and SMT) in natural ways. This dissertation presents a unifying view on these extensions by viewing them as instances of formulas with generalized quantifiers and intensional functions. We extend the first-order stable model semantics by by Ferraris, Lee, and Lifschitz to allow generalized quantifiers, which cover aggregate, DL-atoms, constraints and SMT theory atoms as special cases. Using this unifying framework, we study and relate different extensions of ASP. We also present a tight integration of ASP with SMT, based on which we enhance action language C+ to handle reasoning about continuous changes. Our framework yields a systematic approach to study and extend non-monotonic languages.
ContributorsMeng, Yunsong (Author) / Lee, Joohyung (Thesis advisor) / Ahn, Gail-Joon (Committee member) / Baral, Chitta (Committee member) / Fainekos, Georgios (Committee member) / Lifschitz, Vladimir (Committee member) / Arizona State University (Publisher)
Created2013
Description
Most data cleaning systems aim to go from a given deterministic dirty database to another deterministic but clean database. Such an enterprise pre–supposes that it is in fact possible for the cleaning process to uniquely recover the clean versions of each dirty data tuple. This is not possible in many cases, where the most a cleaning system can do is to generate a (hopefully small) set of clean candidates for each dirty tuple. When the cleaning system is required to output a deterministic database, it is forced to pick one clean candidate (say the "most likely" candidate) per tuple. Such an approach can lead to loss of information. For example, consider a situation where there are three equally likely clean candidates of a dirty tuple. An appealing alternative that avoids such an information loss is to abandon the requirement that the output database be deterministic. In other words, even though the input (dirty) database is deterministic, I allow the reconstructed database to be probabilistic. Although such an approach does avoid the information loss, it also brings forth several challenges. For example, how many alternatives should be kept per tuple in the reconstructed database? Maintaining too many alternatives increases the size of the reconstructed database, and hence the query processing time. Second, while processing queries on the probabilistic database may well increase recall, how would they affect the precision of the query processing? In this thesis, I investigate these questions. My investigation is done in the context of a data cleaning system called BayesWipe that has the capability of producing multiple clean candidates per each dirty tuple, along with the probability that they are the correct cleaned version. I represent these alternatives as tuples in a tuple disjoint probabilistic database, and use the Mystiq system to process queries on it. This probabilistic reconstruction (called BayesWipe–PDB) is compared to a deterministic reconstruction (called BayesWipe–DET)—where the most likely clean candidate for each tuple is chosen, and the rest of the alternatives discarded.
ContributorsRihan, Preet Inder Singh (Author) / Kambhampati, Subbarao (Thesis advisor) / Liu, Huan (Committee member) / Davulcu, Hasan (Committee member) / Arizona State University (Publisher)
Created2013
Description
Recent efforts in data cleaning have focused mostly on problems like data deduplication, record matching, and data standardization; few of these focus on fixing incorrect attribute values in tuples. Correcting values in tuples is typically performed by a minimum cost repair of tuples that violate static constraints like CFDs (which have to be provided by domain experts, or learned from a clean sample of the database). In this thesis, I provide a method for correcting individual attribute values in a structured database using a Bayesian generative model and a statistical error model learned from the noisy database directly. I thus avoid the necessity for a domain expert or master data. I also show how to efficiently perform consistent query answering using this model over a dirty database, in case write permissions to the database are unavailable. A Map-Reduce architecture to perform this computation in a distributed manner is also shown. I evaluate these methods over both synthetic and real data.
ContributorsDe, Sushovan (Author) / Kambhampati, Subbarao (Thesis advisor) / Chen, Yi (Committee member) / Candan, K. Selcuk (Committee member) / Liu, Huan (Committee member) / Arizona State University (Publisher)
Created2014
Description
A statement appearing in social media provides a very significant challenge for determining the provenance of the statement. Provenance describes the origin, custody, and ownership of something. Most statements appearing in social media are not published with corresponding provenance data. However, the same characteristics that make the social media environment challenging, including the massive amounts of data available, large numbers of users, and a highly dynamic environment, provide unique and untapped opportunities for solving the provenance problem for social media. Current approaches for tracking provenance data do not scale for online social media and consequently there is a gap in provenance methodologies and technologies providing exciting research opportunities. The guiding vision is the use of social media information itself to realize a useful amount of provenance data for information in social media. This departs from traditional approaches for data provenance which rely on a central store of provenance information. The contemporary online social media environment is an enormous and constantly updated "central store" that can be mined for provenance information that is not readily made available to the average social media user. This research introduces an approach and builds a foundation aimed at realizing a provenance data capability for social media users that is not accessible today.
ContributorsBarbier, Geoffrey P (Author) / Liu, Huan (Thesis advisor) / Bell, Herbert (Committee member) / Li, Baoxin (Committee member) / Sen, Arunabha (Committee member) / Arizona State University (Publisher)
Created2011
Description
Different logic-based knowledge representation formalisms have different limitations either with respect to expressivity or with respect to computational efficiency. First-order logic, which is the basis of Description Logics (DLs), is not suitable for defeasible reasoning due to its monotonic nature. The nonmonotonic formalisms that extend first-order logic, such as circumscription and default logic, are expressive but lack efficient implementations. The nonmonotonic formalisms that are based on the declarative logic programming approach, such as Answer Set Programming (ASP), have efficient implementations but are not expressive enough for representing and reasoning with open domains. This dissertation uses the first-order stable model semantics, which extends both first-order logic and ASP, to relate circumscription to ASP, and to integrate DLs and ASP, thereby partially overcoming the limitations of the formalisms. By exploiting the relationship between circumscription and ASP, well-known action formalisms, such as the situation calculus, the event calculus, and Temporal Action Logics, are reformulated in ASP. The advantages of these reformulations are shown with respect to the generality of the reasoning tasks that can be handled and with respect to the computational efficiency. The integration of DLs and ASP presented in this dissertation provides a framework for integrating rules and ontologies for the semantic web. This framework enables us to perform nonmonotonic reasoning with DL knowledge bases. Observing the need to integrate action theories and ontologies, the above results are used to reformulate the problem of integrating action theories and ontologies as a problem of integrating rules and ontologies, thus enabling us to use the computational tools developed in the context of the latter for the former.
ContributorsPalla, Ravi (Author) / Lee, Joohyung (Thesis advisor) / Baral, Chitta (Committee member) / Kambhampati, Subbarao (Committee member) / Lifschitz, Vladimir (Committee member) / Arizona State University (Publisher)
Created2012
Description
As the information available to lay users through autonomous data sources continues to increase, mediators become important to ensure that the wealth of information available is tapped effectively. A key challenge that these information mediators need to handle is the varying levels of incompleteness in the underlying databases in terms of missing attribute values. Existing approaches such as Query Processing over Incomplete Autonomous Databases (QPIAD) aim to mine and use Approximate Functional Dependencies (AFDs) to predict and retrieve relevant incomplete tuples. These approaches make independence assumptions about missing values--which critically hobbles their performance when there are tuples containing missing values for multiple correlated attributes. In this thesis, I present a principled probabilis- tic alternative that views an incomplete tuple as defining a distribution over the complete tuples that it stands for. I learn this distribution in terms of Bayes networks. My approach involves min- ing/"learning" Bayes networks from a sample of the database, and using it do both imputation (predict a missing value) and query rewriting (retrieve relevant results with incompleteness on the query-constrained attributes, when the data sources are autonomous). I present empirical studies to demonstrate that (i) at higher levels of incompleteness, when multiple attribute values are missing, Bayes networks do provide a significantly higher classification accuracy and (ii) the relevant possible answers retrieved by the queries reformulated using Bayes networks provide higher precision and recall than AFDs while keeping query processing costs manageable.
ContributorsRaghunathan, Rohit (Author) / Kambhampati, Subbarao (Thesis advisor) / Liu, Huan (Committee member) / Lee, Joohyung (Committee member) / Arizona State University (Publisher)
Created2011
Description
Knowledge representation and reasoning is a prominent subject of study within the field of artificial intelligence that is concerned with the symbolic representation of knowledge in such a way to facilitate automated reasoning about this knowledge. Often in real-world domains, it is necessary to perform defeasible reasoning when representing default behaviors of systems. Answer Set Programming is a widely-used knowledge representation framework that is well-suited for such reasoning tasks and has been successfully applied to practical domains due to efficient computation through grounding--a process that replaces variables with variable-free terms--and propositional solvers similar to SAT solvers. However, some domains provide a challenge for grounding-based methods such as domains requiring reasoning about continuous time or resources.
To address these domains, there have been several proposals to achieve efficiency through loose integrations with efficient declarative solvers such as constraint solvers or satisfiability modulo theories solvers. While these approaches successfully avoid substantial grounding, due to the loose integration, they are not suitable for performing defeasible reasoning on functions. As a result, this expressive reasoning on functions must either be performed using predicates to simulate the functions or in a way that is not elaboration tolerant. Neither compromise is reasonable; the former suffers from the grounding bottleneck when domains are large as is often the case in real-world domains while the latter necessitates encodings to be non-trivially modified for elaborations.
This dissertation presents a novel framework called Answer Set Programming Modulo Theories (ASPMT) that is a tight integration of the stable model semantics and satisfiability modulo theories. This framework both supports defeasible reasoning about functions and alleviates the grounding bottleneck. Combining the strengths of Answer Set Programming and satisfiability modulo theories enables efficient continuous reasoning while still supporting rich reasoning features such as reasoning about defaults and reasoning in domains with incomplete knowledge. This framework is realized in two prototype implementations called MVSM and ASPMT2SMT, and the latter was recently incorporated into a non-monotonic spatial reasoning system. To define the semantics of this framework, we extend the first-order stable model semantics by Ferraris, Lee and Lifschitz to allow "intensional functions" and provide analyses of the theoretical properties of this new formalism and on the relationships between this and existing approaches.
To address these domains, there have been several proposals to achieve efficiency through loose integrations with efficient declarative solvers such as constraint solvers or satisfiability modulo theories solvers. While these approaches successfully avoid substantial grounding, due to the loose integration, they are not suitable for performing defeasible reasoning on functions. As a result, this expressive reasoning on functions must either be performed using predicates to simulate the functions or in a way that is not elaboration tolerant. Neither compromise is reasonable; the former suffers from the grounding bottleneck when domains are large as is often the case in real-world domains while the latter necessitates encodings to be non-trivially modified for elaborations.
This dissertation presents a novel framework called Answer Set Programming Modulo Theories (ASPMT) that is a tight integration of the stable model semantics and satisfiability modulo theories. This framework both supports defeasible reasoning about functions and alleviates the grounding bottleneck. Combining the strengths of Answer Set Programming and satisfiability modulo theories enables efficient continuous reasoning while still supporting rich reasoning features such as reasoning about defaults and reasoning in domains with incomplete knowledge. This framework is realized in two prototype implementations called MVSM and ASPMT2SMT, and the latter was recently incorporated into a non-monotonic spatial reasoning system. To define the semantics of this framework, we extend the first-order stable model semantics by Ferraris, Lee and Lifschitz to allow "intensional functions" and provide analyses of the theoretical properties of this new formalism and on the relationships between this and existing approaches.
ContributorsBartholomew, Michael James (Author) / Lee, Joohyung (Thesis advisor) / Bazzi, Rida (Committee member) / Colbourn, Charles (Committee member) / Fainekos, Georgios (Committee member) / Lifschitz, Vladimir (Committee member) / Arizona State University (Publisher)
Created2016
Description
Goal specification is an important aspect of designing autonomous agents. A goal does not only refer to the set of states for the agent to reach. A goal also defines restrictions on the paths the agent should follow. Temporal logics are widely used in goal specification. However, they lack the ability to represent goals in a non-deterministic domain, goals that change non-monotonically, and goals with preferences. This dissertation defines new goal specification languages by extending temporal logics to address these issues. First considered is the goal specification in non-deterministic domains, in which an agent following a policy leads to a set of paths. A logic is proposed to distinguish paths of the agent from all paths in the domain. In addition, to address the need of comparing policies for finding the best ones, a language capable of quantifying over policies is proposed. As policy structures of agents play an important role in goal specification, languages are also defined by considering different policy structures. Besides, after an agent is given an initial goal, the agent may change its expectations or the domain may change, thus goals that are previously specified may need to be further updated, revised, partially retracted, or even completely changed. Non-monotonic goal specification languages that can make these changes in an elaboration tolerant manner are needed. Two languages that rely on labeling sub-formulas and connecting multiple rules are developed to address non-monotonicity in goal specification. Also, agents may have preferential relations among sub-goals, and the preferential relations may change as agents achieve other sub-goals. By nesting a comparison operator with other temporal operators, a language with dynamic preferences is proposed. Various goals that cannot be expressed in other languages are expressed in the proposed languages. Finally, plans are given for some goals specified in the proposed languages.
ContributorsZhao, Jicheng (Author) / Baral, Chitta (Thesis advisor) / Kambhampati, Subbarao (Committee member) / Lee, Joohyung (Committee member) / Lifschitz, Vladimir (Committee member) / Liu, Huan (Committee member) / Arizona State University (Publisher)
Created2010
Description
Prognostics and health management (PHM) is a method that permits the reliability of a system to be evaluated in its actual application conditions. This work involved developing a robust system to determine the advent of failure. Using the data from the PHM experiment, a model was developed to estimate the prognostic features and build a condition based system based on measured prognostics. To enable prognostics, a framework was developed to extract load parameters required for damage assessment from irregular time-load data. As a part of the methodology, a database engine was built to maintain and monitor the experimental data. This framework helps in significant reduction of the time-load data without compromising features that are essential for damage estimation. A failure precursor based approach was used for remaining life prognostics. The developed system has a throughput of 4MB/sec with 90% latency within 100msec. This work hence provides an overview on Prognostic framework survey, Prognostics Framework architecture and design approach with a robust system implementation.
ContributorsVaradarajan, Gayathri (Author) / Liu, Huan (Thesis advisor) / Ye, Jieping (Committee member) / Davalcu, Hasan (Committee member) / Arizona State University (Publisher)
Created2010