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- Genre: Doctoral Dissertation
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
Biological organisms are made up of cells containing numerous interconnected biochemical processes. Diseases occur when normal functionality of these processes is disrupted, manifesting as disease symptoms. Thus, understanding these biochemical processes and their interrelationships is a primary task in biomedical research and a prerequisite for activities including diagnosing diseases and drug development. Scientists studying these interconnected processes have identified various pathways involved in drug metabolism, diseases, and signal transduction, etc. High-throughput technologies, new algorithms and speed improvements over the last decade have resulted in deeper knowledge about biological systems, leading to more refined pathways. Such pathways tend to be large and complex, making it difficult for an individual to remember all aspects. Thus, computer models are needed to represent and analyze them. The refinement activity itself requires reasoning with a pathway model by posing queries against it and comparing the results against the real biological system. Many existing models focus on structural and/or factoid questions, relying on surface-level information. These are generally not the kind of questions that a biologist may ask someone to test their understanding of biological processes. Examples of questions requiring understanding of biological processes are available in introductory college level biology text books. Such questions serve as a model for the question answering system developed in this thesis. Thus, the main goal of this thesis is to develop a system that allows the encoding of knowledge about biological pathways to answer questions demonstrating understanding of the pathways. To that end, a language is developed to specify a pathway and pose questions against it. Some existing tools are modified and used to accomplish this goal. The utility of the framework developed in this thesis is illustrated with applications in the biological domain. Finally, the question answering system is used in real world applications by extracting pathway knowledge from text and answering questions related to drug development.
ContributorsAnwar, Saadat (Author) / Baral, Chitta (Thesis advisor) / Inoue, Katsumi (Committee member) / Chen, Yi (Committee member) / Davulcu, Hasan (Committee member) / Lee, Joohyung (Committee member) / Arizona State University (Publisher)
Created2014
Description
Current work in planning assumes that user preferences and/or domain dynamics are completely specified in advance, and aims to search for a single solution plan to satisfy these. In many real world scenarios, however, providing a complete specification of user preferences and domain dynamics becomes a time-consuming and error-prone task. More often than not, a user may provide no knowledge or at best partial knowledge of her preferences with respect to a desired plan. Similarly, a domain writer may only be able to determine certain parts, not all, of the model of some actions in a domain. Such modeling issues requires new concepts on what a solution should be, and novel techniques in solving the problem. When user preferences are incomplete, rather than presenting a single plan, the planner must instead provide a set of plans containing one or more plans that are similar to the one that the user prefers. This research first proposes the usage of different measures to capture the quality of such plan sets. These are domain-independent distance measures based on plan elements if no knowledge of the user preferences is given, or the Integrated Preference Function measure in case incomplete knowledge of such preferences is provided. It then investigates various heuristic approaches to generate plan sets in accordance with these measures, and presents empirical results demonstrating the promise of the methods. The second part of this research addresses planning problems with incomplete domain models, specifically those annotated with possible preconditions and effects of actions. It formalizes the notion of plan robustness capturing the probability of success for plans during execution. A method of assessing plan robustness based on the weighted model counting approach is proposed. Two approaches for synthesizing robust plans are introduced. The first one compiles the robust plan synthesis problems to the conformant probabilistic planning problems. The second approximates the robustness measure with lower and upper bounds, incorporating them into a stochastic local search for estimating distance heuristic to a goal state. The resulting planner outperforms a state-of-the-art planner that can handle incomplete domain models in both plan quality and planning time.
ContributorsNguyễn, Tuấn Anh (Author) / Kambhampati, Subbarao (Thesis advisor) / Baral, Chitta (Committee member) / Do, Minh (Committee member) / Lee, Joohyung (Committee member) / Smith, David E. (Committee member) / Arizona State University (Publisher)
Created2014
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
Most embedded applications are constructed with multiple threads to handle concurrent events. For optimization and debugging of the programs, dynamic program analysis is widely used to collect execution information while the program is running. Unfortunately, the non-deterministic behavior of multithreaded embedded software makes the dynamic analysis difficult. In addition, instrumentation overhead for gathering execution information may change the execution of a program, and lead to distorted analysis results, i.e., probe effect. This thesis presents a framework that tackles the non-determinism and probe effect incurred in dynamic analysis of embedded software. The thesis largely consists of three parts. First of all, we discusses a deterministic replay framework to provide reproducible execution. Once a program execution is recorded, software instrumentation can be safely applied during replay without probe effect. Second, a discussion of probe effect is presented and a simulation-based analysis is proposed to detect execution changes of a program caused by instrumentation overhead. The simulation-based analysis examines if the recording instrumentation changes the original program execution. Lastly, the thesis discusses data race detection algorithms that help to remove data races for correctness of the replay and the simulation-based analysis. The focus is to make the detection efficient for C/C++ programs, and to increase scalability of the detection on multi-core machines.
ContributorsSong, Young Wn (Author) / Lee, Yann-Hang (Thesis advisor) / Shrivastava, Aviral (Committee member) / Fainekos, Georgios (Committee member) / Lee, Joohyung (Committee member) / Arizona State University (Publisher)
Created2015
Description
The Web is one of the most exciting and dynamic areas of development in today’s technology. However, with such activity, innovation, and ubiquity have come a set of new challenges for digital forensic examiners, making their jobs even more difficult. For examiners to become as effective with evidence from the Web as they currently are with more traditional evidence, they need (1) methods that guide them to know how to approach this new type of evidence and (2) tools that accommodate web environments’ unique characteristics.
In this dissertation, I present my research to alleviate the difficulties forensic examiners currently face with respect to evidence originating from web environments. First, I introduce a framework for web environment forensics, which elaborates on and addresses the key challenges examiners face and outlines a method for how to approach web-based evidence. Next, I describe my work to identify extensions installed on encrypted web thin clients using only a sound understanding of these systems’ inner workings and the metadata of the encrypted files. Finally, I discuss my approach to reconstructing the timeline of events on encrypted web thin clients by using service provider APIs as a proxy for directly analyzing the device. In each of these research areas, I also introduce structured formats that I customized to accommodate the unique features of the evidence sources while also facilitating tool interoperability and information sharing.
In this dissertation, I present my research to alleviate the difficulties forensic examiners currently face with respect to evidence originating from web environments. First, I introduce a framework for web environment forensics, which elaborates on and addresses the key challenges examiners face and outlines a method for how to approach web-based evidence. Next, I describe my work to identify extensions installed on encrypted web thin clients using only a sound understanding of these systems’ inner workings and the metadata of the encrypted files. Finally, I discuss my approach to reconstructing the timeline of events on encrypted web thin clients by using service provider APIs as a proxy for directly analyzing the device. In each of these research areas, I also introduce structured formats that I customized to accommodate the unique features of the evidence sources while also facilitating tool interoperability and information sharing.
ContributorsMabey, Michael Kent (Author) / Ahn, Gail-Joon (Thesis advisor) / Doupe, Adam (Thesis advisor) / Yau, Stephen S. (Committee member) / Lee, Joohyung (Committee member) / Zhao, Ziming (Committee member) / Arizona State University (Publisher)
Created2017
Description
Reasoning about actions forms the basis of many tasks such as prediction, planning, and diagnosis in a dynamic domain. Within the reasoning about actions community, a broad class of languages, called action languages, has been developed together with a methodology for their use in representing and reasoning about dynamic domains. With a few notable exceptions, the focus of these efforts has largely centered around single-agent systems. Agents rarely operate in a vacuum however, and almost in parallel, substantial work has been done within the dynamic epistemic logic community towards understanding how the actions of an agent may effect not just his own knowledge and/or beliefs, but those of his fellow agents as well. What is less understood by both communities is how to represent and reason about both the direct and indirect effects of both ontic and epistemic actions within a multi-agent setting. This dissertation presents ongoing research towards a framework for representing and reasoning about dynamic multi-agent domains involving both classes of actions.
The contributions of this work are as follows: the formulation of a precise mathematical model of a dynamic multi-agent domain based on the notion of a transition diagram; the development of the multi-agent action languages mA+ and mAL based upon this model, as well as preliminary investigations of their properties and implementations via logic programming under the answer set semantics; precise formulations of the temporal projection, and planning problems within a multi-agent context; and an investigation of the application of the proposed approach to the representation of, and reasoning about, scenarios involving the modalities of knowledge and belief.
The contributions of this work are as follows: the formulation of a precise mathematical model of a dynamic multi-agent domain based on the notion of a transition diagram; the development of the multi-agent action languages mA+ and mAL based upon this model, as well as preliminary investigations of their properties and implementations via logic programming under the answer set semantics; precise formulations of the temporal projection, and planning problems within a multi-agent context; and an investigation of the application of the proposed approach to the representation of, and reasoning about, scenarios involving the modalities of knowledge and belief.
ContributorsGelfond, Gregory (Author) / Baral, Chitta (Thesis advisor) / Kambhampati, Subbarao (Committee member) / Lee, Joohyung (Committee member) / Moss, Larry (Committee member) / Cao Son, Tran (Committee member) / Arizona State University (Publisher)
Created2018
Description
Image Understanding is a long-established discipline in computer vision, which encompasses a body of advanced image processing techniques, that are used to locate (“where”), characterize and recognize (“what”) objects, regions, and their attributes in the image. However, the notion of “understanding” (and the goal of artificial intelligent machines) goes beyond factual recall of the recognized components and includes reasoning and thinking beyond what can be seen (or perceived). Understanding is often evaluated by asking questions of increasing difficulty. Thus, the expected functionalities of an intelligent Image Understanding system can be expressed in terms of the functionalities that are required to answer questions about an image. Answering questions about images require primarily three components: Image Understanding, question (natural language) understanding, and reasoning based on knowledge. Any question, asking beyond what can be directly seen, requires modeling of commonsense (or background/ontological/factual) knowledge and reasoning.
Knowledge and reasoning have seen scarce use in image understanding applications. In this thesis, we demonstrate the utilities of incorporating background knowledge and using explicit reasoning in image understanding applications. We first present a comprehensive survey of the previous work that utilized background knowledge and reasoning in understanding images. This survey outlines the limited use of commonsense knowledge in high-level applications. We then present a set of vision and reasoning-based methods to solve several applications and show that these approaches benefit in terms of accuracy and interpretability from the explicit use of knowledge and reasoning. We propose novel knowledge representations of image, knowledge acquisition methods, and a new implementation of an efficient probabilistic logical reasoning engine that can utilize publicly available commonsense knowledge to solve applications such as visual question answering, image puzzles. Additionally, we identify the need for new datasets that explicitly require external commonsense knowledge to solve. We propose the new task of Image Riddles, which requires a combination of vision, and reasoning based on ontological knowledge; and we collect a sufficiently large dataset to serve as an ideal testbed for vision and reasoning research. Lastly, we propose end-to-end deep architectures that can combine vision, knowledge and reasoning modules together and achieve large performance boosts over state-of-the-art methods.
Knowledge and reasoning have seen scarce use in image understanding applications. In this thesis, we demonstrate the utilities of incorporating background knowledge and using explicit reasoning in image understanding applications. We first present a comprehensive survey of the previous work that utilized background knowledge and reasoning in understanding images. This survey outlines the limited use of commonsense knowledge in high-level applications. We then present a set of vision and reasoning-based methods to solve several applications and show that these approaches benefit in terms of accuracy and interpretability from the explicit use of knowledge and reasoning. We propose novel knowledge representations of image, knowledge acquisition methods, and a new implementation of an efficient probabilistic logical reasoning engine that can utilize publicly available commonsense knowledge to solve applications such as visual question answering, image puzzles. Additionally, we identify the need for new datasets that explicitly require external commonsense knowledge to solve. We propose the new task of Image Riddles, which requires a combination of vision, and reasoning based on ontological knowledge; and we collect a sufficiently large dataset to serve as an ideal testbed for vision and reasoning research. Lastly, we propose end-to-end deep architectures that can combine vision, knowledge and reasoning modules together and achieve large performance boosts over state-of-the-art methods.
ContributorsAditya, Somak (Author) / Baral, Chitta (Thesis advisor) / Yang, Yezhou (Thesis advisor) / Aloimonos, Yiannis (Committee member) / Lee, Joohyung (Committee member) / Li, Baoxin (Committee member) / Arizona State University (Publisher)
Created2018
Description
Knowledge Representation (KR) is one of the prominent approaches to Artificial Intelligence (AI) that is concerned with representing knowledge in a form that computer systems can utilize to solve complex problems. Answer Set Programming (ASP), based on the stable model semantics, is a widely-used KR framework that facilitates elegant and efficient representations for many problem domains that require complex reasoning.
However, while ASP is effective on deterministic problem domains, it is not suitable for applications involving quantitative uncertainty, for example, those that require probabilistic reasoning. Furthermore, it is hard to utilize information that can be statistically induced from data with ASP problem modeling.
This dissertation presents the language LP^MLN, which is a probabilistic extension of the stable model semantics with the concept of weighted rules, inspired by Markov Logic. An LP^MLN program defines a probability distribution over "soft" stable models, which may not satisfy all rules, but the more rules with the bigger weights they satisfy, the bigger their probabilities. LP^MLN takes advantage of both ASP and Markov Logic in a single framework, allowing representation of problems that require both logical and probabilistic reasoning in an intuitive and elaboration tolerant way.
This dissertation establishes formal relations between LP^MLN and several other formalisms, discusses inference and weight learning algorithms under LP^MLN, and presents systems implementing the algorithms. LP^MLN systems can be used to compute other languages translatable into LP^MLN.
The advantage of LP^MLN for probabilistic reasoning is illustrated by a probabilistic extension of the action language BC+, called pBC+, defined as a high-level notation of LP^MLN for describing transition systems. Various probabilistic reasoning about transition systems, especially probabilistic diagnosis, can be modeled in pBC+ and computed using LP^MLN systems. pBC+ is further extended with the notion of utility, through a decision-theoretic extension of LP^MLN, and related with Markov Decision Process (MDP) in terms of policy optimization problems. pBC+ can be used to represent (PO)MDP in a succinct and elaboration tolerant way, which enables planning with (PO)MDP algorithms in action domains whose description requires rich KR constructs, such as recursive definitions and indirect effects of actions.
However, while ASP is effective on deterministic problem domains, it is not suitable for applications involving quantitative uncertainty, for example, those that require probabilistic reasoning. Furthermore, it is hard to utilize information that can be statistically induced from data with ASP problem modeling.
This dissertation presents the language LP^MLN, which is a probabilistic extension of the stable model semantics with the concept of weighted rules, inspired by Markov Logic. An LP^MLN program defines a probability distribution over "soft" stable models, which may not satisfy all rules, but the more rules with the bigger weights they satisfy, the bigger their probabilities. LP^MLN takes advantage of both ASP and Markov Logic in a single framework, allowing representation of problems that require both logical and probabilistic reasoning in an intuitive and elaboration tolerant way.
This dissertation establishes formal relations between LP^MLN and several other formalisms, discusses inference and weight learning algorithms under LP^MLN, and presents systems implementing the algorithms. LP^MLN systems can be used to compute other languages translatable into LP^MLN.
The advantage of LP^MLN for probabilistic reasoning is illustrated by a probabilistic extension of the action language BC+, called pBC+, defined as a high-level notation of LP^MLN for describing transition systems. Various probabilistic reasoning about transition systems, especially probabilistic diagnosis, can be modeled in pBC+ and computed using LP^MLN systems. pBC+ is further extended with the notion of utility, through a decision-theoretic extension of LP^MLN, and related with Markov Decision Process (MDP) in terms of policy optimization problems. pBC+ can be used to represent (PO)MDP in a succinct and elaboration tolerant way, which enables planning with (PO)MDP algorithms in action domains whose description requires rich KR constructs, such as recursive definitions and indirect effects of actions.
ContributorsWang, Yi (Author) / Lee, Joohyung (Thesis advisor) / Baral, Chitta (Committee member) / Kambhampati, Subbarao (Committee member) / Natarajan, Sriraam (Committee member) / Srivastava, Siddharth (Committee member) / Arizona State University (Publisher)
Created2019
Description
Identifying chemical compounds that inhibit bacterial infection has recently gained a considerable amount of attention given the increased number of highly resistant bacteria and the serious health threat it poses around the world. With the development of automated microscopy and image analysis systems, the process of identifying novel therapeutic drugs can generate an immense amount of data - easily reaching terabytes worth of information. Despite increasing the vast amount of data that is currently generated, traditional analytical methods have not increased the overall success rate of identifying active chemical compounds that eventually become novel therapeutic drugs. Moreover, multispectral imaging has become ubiquitous in drug discovery due to its ability to provide valuable information on cellular and sub-cellular processes using florescent reagents. These reagents are often costly and toxic to cells over an extended period of time causing limitations in experimental design. Thus, there is a significant need to develop a more efficient process of identifying active chemical compounds.
This dissertation introduces novel machine learning methods based on parallelized cellomics to analyze interactions between cells, bacteria, and chemical compounds while reducing the use of fluorescent reagents. Machine learning analysis using image-based high-content screening (HCS) data is compartmentalized into three primary components: (1) \textit{Image Analytics}, (2) \textit{Phenotypic Analytics}, and (3) \textit{Compound Analytics}. A novel software analytics tool called the Insights project is also introduced. The Insights project fully incorporates distributed processing, high performance computing, and database management that can rapidly and effectively utilize and store massive amounts of data generated using HCS biological assessments (bioassays). It is ideally suited for parallelized cellomics in high dimensional space.
Results demonstrate that a parallelized cellomics approach increases the quality of a bioassay while vastly decreasing the need for control data. The reduction in control data leads to less fluorescent reagent consumption. Furthermore, a novel proposed method that uses single-cell data points is proven to identify known active chemical compounds with a high degree of accuracy, despite traditional quality control measurements indicating the bioassay to be of poor quality. This, ultimately, decreases the time and resources needed in optimizing bioassays while still accurately identifying active compounds.
This dissertation introduces novel machine learning methods based on parallelized cellomics to analyze interactions between cells, bacteria, and chemical compounds while reducing the use of fluorescent reagents. Machine learning analysis using image-based high-content screening (HCS) data is compartmentalized into three primary components: (1) \textit{Image Analytics}, (2) \textit{Phenotypic Analytics}, and (3) \textit{Compound Analytics}. A novel software analytics tool called the Insights project is also introduced. The Insights project fully incorporates distributed processing, high performance computing, and database management that can rapidly and effectively utilize and store massive amounts of data generated using HCS biological assessments (bioassays). It is ideally suited for parallelized cellomics in high dimensional space.
Results demonstrate that a parallelized cellomics approach increases the quality of a bioassay while vastly decreasing the need for control data. The reduction in control data leads to less fluorescent reagent consumption. Furthermore, a novel proposed method that uses single-cell data points is proven to identify known active chemical compounds with a high degree of accuracy, despite traditional quality control measurements indicating the bioassay to be of poor quality. This, ultimately, decreases the time and resources needed in optimizing bioassays while still accurately identifying active compounds.
ContributorsTrevino, Robert (Author) / Liu, Huan (Thesis advisor) / Lamkin, Thomas J (Committee member) / He, Jingrui (Committee member) / Lee, Joohyung (Committee member) / Arizona State University (Publisher)
Created2016