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- All Subjects: artificial intelligence
- Creators: Lee, Joohyung
- Creators: VanLehn, Kurt
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
There has been a lot of research in the field of artificial intelligence about thinking machines. Alan Turing proposed a test to observe a machine's intelligent behaviour with respect to natural language conversation. The Winograd schema challenge is suggested as an alternative, to the Turing test. It needs inferencing capabilities, reasoning abilities and background knowledge to get the answer right. It involves a coreference resolution task in which a machine is given a sentence containing a situation which involves two entities, one pronoun and some more information about the situation and the machine has to come up with the right resolution of a pronoun to one of the entities. The complexity of the task is increased with the fact that the Winograd sentences are not constrained by one domain or specific sentence structure and it also contains a lot of human proper names. This modification makes the task of association of entities, to one particular word in the sentence, to derive the answer, difficult. I have developed a pronoun resolver system for the confined domain Winograd sentences. I have developed a classifier or filter which takes input sentences and decides to accept or reject them based on a particular criteria. Once the sentence is accepted. I run parsers on it to obtain the detailed analysis. Furthermore I have developed four answering modules which use world knowledge and inferencing mechanisms to try and resolve the pronoun. The four techniques I use are : ConceptNet knowledgebase, Search engine pattern counts,Narrative event chains and sentiment analysis. I have developed a particular aggregation mechanism for the answers from these modules to arrive at a final answer. I have used caching technique for the association relations that I obtain for different modules, so as to boost the performance. I run my system on the standard ‘nyu dataset’ of Winograd sentences and questions. This dataset is then restricted, by my classifier, to 90 sentences. I evaluate my system on this 90 sentence dataset. When I compare my results against the state of the art system on the same dataset, I get nearly 4.5 % improvement in the restricted domain.
ContributorsBudukh, Tejas Ulhas (Author) / Baral, Chitta (Thesis advisor) / VanLehn, Kurt (Committee member) / Davulcu, Hasan (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
In a healthcare setting, the Sterile Processing Department (SPD) provides ancillary services to the Operating Room (OR), Emergency Room, Labor & Delivery, and off-site clinics. SPD's function is to reprocess reusable surgical instruments and return them to their home departments. The management of surgical instruments and medical devices can impact patient safety and hospital revenue. Any time instrumentation or devices are not available or are not fit for use, patient safety and revenue can be negatively impacted. One step of the instrument reprocessing cycle is sterilization. Steam sterilization is the sterilization method used for the majority of surgical instruments and is preferred to immediate use steam sterilization (IUSS) because terminally sterilized items can be stored until needed. IUSS Items must be used promptly and cannot be stored for later use. IUSS is intended for emergency situations and not as regular course of action. Unfortunately, IUSS is used to compensate for inadequate inventory levels, scheduling conflicts, and miscommunications. If IUSS is viewed as an adverse event, then monitoring IUSS incidences can help healthcare organizations meet patient safety goals and financial goals along with aiding in process improvement efforts. This work recommends statistical process control methods to IUSS incidents and illustrates the use of control charts for IUSS occurrences through a case study and analysis of the control charts for data from a health care provider. Furthermore, this work considers the application of data mining methods to IUSS occurrences and presents a representative example of data mining to the IUSS occurrences. This extends the application of statistical process control and data mining in healthcare applications.
ContributorsWeart, Gail (Author) / Runger, George C. (Thesis advisor) / Li, Jing (Committee member) / Shunk, Dan (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
Turing test has been a benchmark scale for measuring the human level intelligence in computers since it was proposed by Alan Turing in 1950. However, for last 60 years, the applications such as ELIZA, PARRY, Cleverbot and Eugene Goostman, that claimed to pass the test. These applications are either based on tricks to fool humans on a textual chat based test or there has been a disagreement between AI communities on them passing the test. This has led to the school of thought that it might not be the ideal test for predicting the human level intelligence in machines.
Consequently, the Winograd Schema Challenge has been suggested as an alternative to the Turing test. As opposed to deciding the intelligent behavior with the help of chat servers, like it was done in the Turing test, the Winograd Schema Challenge is a question answering test. It consists of sentence and question pairs such that the answer to the question depends on the resolution of a definite pronoun or adjective in the sentence. The answers are fairly intuitive for humans but they are difficult for machines because it requires some sort of background or commonsense knowledge about the sentence.
In this thesis, I propose a novel technique to solve the Winograd Schema Challenge. The technique has three basic modules at its disposal, namely, a Semantic Parser that parses the English text (both sentences and questions) into a formal representation, an Automatic Background Knowledge Extractor that extracts the Background Knowledge pertaining to the given Winograd sentence, and an Answer Set Programming Reasoning Engine that reasons on the given Winograd sentence and the corresponding Background Knowledge. The applicability of the technique is illustrated by solving a subset of Winograd Schema Challenge pertaining to a certain type of Background Knowledge. The technique is evaluated on the subset and a notable accuracy is achieved.
Consequently, the Winograd Schema Challenge has been suggested as an alternative to the Turing test. As opposed to deciding the intelligent behavior with the help of chat servers, like it was done in the Turing test, the Winograd Schema Challenge is a question answering test. It consists of sentence and question pairs such that the answer to the question depends on the resolution of a definite pronoun or adjective in the sentence. The answers are fairly intuitive for humans but they are difficult for machines because it requires some sort of background or commonsense knowledge about the sentence.
In this thesis, I propose a novel technique to solve the Winograd Schema Challenge. The technique has three basic modules at its disposal, namely, a Semantic Parser that parses the English text (both sentences and questions) into a formal representation, an Automatic Background Knowledge Extractor that extracts the Background Knowledge pertaining to the given Winograd sentence, and an Answer Set Programming Reasoning Engine that reasons on the given Winograd sentence and the corresponding Background Knowledge. The applicability of the technique is illustrated by solving a subset of Winograd Schema Challenge pertaining to a certain type of Background Knowledge. The technique is evaluated on the subset and a notable accuracy is achieved.
ContributorsSharma, Arpita (Author) / Baral, Chita (Thesis advisor) / Lee, Joohyung (Committee member) / Pon-Barry, Heather (Committee member) / Arizona State University (Publisher)
Created2014
Description
This dissertation transforms a set of system complexity reduction problems to feature selection problems. Three systems are considered: classification based on association rules, network structure learning, and time series classification. Furthermore, two variable importance measures are proposed to reduce the feature selection bias in tree models. Associative classifiers can achieve high accuracy, but the combination of many rules is difficult to interpret. Rule condition subset selection (RCSS) methods for associative classification are considered. RCSS aims to prune the rule conditions into a subset via feature selection. The subset then can be summarized into rule-based classifiers. Experiments show that classifiers after RCSS can substantially improve the classification interpretability without loss of accuracy. An ensemble feature selection method is proposed to learn Markov blankets for either discrete or continuous networks (without linear, Gaussian assumptions). The method is compared to a Bayesian local structure learning algorithm and to alternative feature selection methods in the causal structure learning problem. Feature selection is also used to enhance the interpretability of time series classification. Existing time series classification algorithms (such as nearest-neighbor with dynamic time warping measures) are accurate but difficult to interpret. This research leverages the time-ordering of the data to extract features, and generates an effective and efficient classifier referred to as a time series forest (TSF). The computational complexity of TSF is only linear in the length of time series, and interpretable features can be extracted. These features can be further reduced, and summarized for even better interpretability. Lastly, two variable importance measures are proposed to reduce the feature selection bias in tree-based ensemble models. It is well known that bias can occur when predictor attributes have different numbers of values. Two methods are proposed to solve the bias problem. One uses an out-of-bag sampling method called OOBForest, and the other, based on the new concept of a partial permutation test, is called a pForest. Experimental results show the existing methods are not always reliable for multi-valued predictors, while the proposed methods have advantages.
ContributorsDeng, Houtao (Author) / Runger, George C. (Thesis advisor) / Lohr, Sharon L (Committee member) / Pan, Rong (Committee member) / Zhang, Muhong (Committee member) / Arizona State University (Publisher)
Created2011
Description
Action language C+ is a formalism for describing properties of actions, which is based on nonmonotonic causal logic. The definite fragment of C+ is implemented in the Causal Calculator (CCalc), which is based on the reduction of nonmonotonic causal logic to propositional logic. This thesis describes the language of CCalc in terms of answer set programming (ASP), based on the translation of nonmonotonic causal logic to formulas under the stable model semantics. I designed a standard library which describes the constructs of the input language of CCalc in terms of ASP, allowing a simple modular method to represent CCalc input programs in the language of ASP. Using the combination of system F2LP and answer set solvers, this method achieves functionality close to that of CCalc while taking advantage of answer set solvers to yield efficient computation that is orders of magnitude faster than CCalc for many benchmark examples. In support of this, I created an automated translation system Cplus2ASP that implements the translation and encoding method and automatically invokes the necessary software to solve the translated input programs.
ContributorsCasolary, Michael (Author) / Lee, Joohyung (Thesis advisor) / Ahn, Gail-Joon (Committee member) / Baral, Chitta (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
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