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- Genre: Masters Thesis
- Creators: Baral, Chitta
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
Linear Temporal Logic is gaining increasing popularity as a high level specification language for robot motion planning due to its expressive power and scalability of LTL control synthesis algorithms. This formalism, however, requires expert knowledge and makes it inaccessible to non-expert users. This thesis introduces a graphical specification environment to create high level motion plans to control robots in the field by converting a visual representation of the motion/task plan into a Linear Temporal Logic (LTL) specification. The visual interface is built on the Android tablet platform and provides functionality to create task plans through a set of well defined gestures and on screen controls. It uses the notion of waypoints to quickly and efficiently describe the motion plan and enables a variety of complex Linear Temporal Logic specifications to be described succinctly and intuitively by the user without the need for the knowledge and understanding of LTL specification. Thus, it opens avenues for its use by personnel in military, warehouse management, and search and rescue missions. This thesis describes the construction of LTL for various scenarios used for robot navigation using the visual interface developed and leverages the use of existing LTL based motion planners to carry out the task plan by a robot.
ContributorsSrinivas, Shashank (Author) / Fainekos, Georgios (Thesis advisor) / Baral, Chitta (Committee member) / Burleson, Winslow (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
Currently, to interact with computer based systems one needs to learn the specific interface language of that system. In most cases, interaction would be much easier if it could be done in natural language. For that, we will need a module which understands natural language and automatically translates it to the interface language of the system. NL2KR (Natural language to knowledge representation) v.1 system is a prototype of such a system. It is a learning based system that learns new meanings of words in terms of lambda-calculus formulas given an initial lexicon of some words and their meanings and a training corpus of sentences with their translations. As a part of this thesis, we take the prototype NL2KR v.1 system and enhance various components of it to make it usable for somewhat substantial and useful interface languages. We revamped the lexicon learning components, Inverse-lambda and Generalization modules, and redesigned the lexicon learning algorithm which uses these components to learn new meanings of words. Similarly, we re-developed an inbuilt parser of the system in Answer Set Programming (ASP) and also integrated external parser with the system. Apart from this, we added some new rich features like various system configurations and memory cache in the learning component of the NL2KR system. These enhancements helped in learning more meanings of the words, boosted performance of the system by reducing the computation time by a factor of 8 and improved the usability of the system. We evaluated the NL2KR system on iRODS domain. iRODS is a rule-oriented data system, which helps in managing large set of computer files using policies. This system provides a Rule-Oriented interface langauge whose syntactic structure is like any procedural programming language (eg. C). However, direct translation of natural language (NL) to this interface language is difficult. So, for automatic translation of NL to this language, we define a simple intermediate Policy Declarative Language (IPDL) to represent the knowledge in the policies, which then can be directly translated to iRODS rules. We develop a corpus of 100 policy statements and manually translate them to IPDL langauge. This corpus is then used for the evaluation of NL2KR system. We performed 10 fold cross validation on the system. Furthermore, using this corpus, we illustrate how different components of our NL2KR system work.
ContributorsKumbhare, Kanchan Ravishankar (Author) / Baral, Chitta (Thesis advisor) / Ye, Jieping (Committee member) / Li, Baoxin (Committee member) / Arizona State University (Publisher)
Created2013
Description
This study investigated the efficacy of Early Head Start home-based, center-based and mixed-approach programs on cognitive, language and behavioral outcomes at different levels of cumulative environmental risk. Early Head Start is a federal program that provides low-income families and their children from birth to age three with childcare, parenting education, healthcare and other family supports. As part of Early Head Start's initiation, a program evaluation was begun involving 3,001 children from 17 programs around the country. Half of the children were randomly assigned to the control group, who received no Early Head Start services. Data were collected through program application and enrollment forms, interviews of parents and child and family assessments. Almost all of the children's primary caretakers were mothers, ranging in age from 18 to 26. One-third were African American, one-third white, and one-fourth Hispanic. Almost half of the parents did not have a high school diploma at the time of enrollment, and most of the families received public support of some kind. For each child, a multiple environmental risk score was calculated, which was the sum of 10 possible environmental risks. Each of four outcomes was regressed onto the ten risks individually and also as a cumulative risk index along with program type and covariates. There were significant negative relations of accumulated risk to reductions in reasoning, spatial ability and vocabulary and increased behavior problems. Children with at least eight risks scored 1.48 standard deviations lower on reasoning ability and vocabulary, .48 standard deviations lower on spatial ability and .48 standard deviations higher on behavior problems. The home-based program showed significant benefit for reasoning and vocabulary. Versus the control group, home-based programs increased average reasoning scores by .24 of a standard deviation and increased vocabulary by .14 of a standard deviation. There was no significant difference in program benefits at different levels of risk. This suggests that for reasoning and vocabulary, the home-based program is promotive because the degree of benefit Early Head Start appears to provide is consistent across all levels of risk for the set of risks and outcomes examined in this study.
ContributorsBudinger, Susan (Author) / Bradley, Robert H (Thesis advisor) / Doane Sampey, Leah D (Committee member) / Valiente, Carlos (Committee member) / Arizona State University (Publisher)
Created2012
Description
While developing autonomous intelligent robots has been the goal of many research programs, a more practical application involving intelligent robots is the formation of teams consisting of both humans and robots. An example of such an application is search and rescue operations where robots commanded by humans are sent to environments too dangerous for humans. For such human-robot interaction, natural language is considered a good communication medium as it allows humans with less training about the robot's internal language to be able to command and interact with the robot. However, any natural language communication from the human needs to be translated to a formal language that the robot can understand. Similarly, before the robot can communicate (in natural language) with the human, it needs to formulate its communique in some formal language which then gets translated into natural language. In this paper, I develop a high level language for communication between humans and robots and demonstrate various aspects through a robotics simulation. These language constructs borrow some ideas from action execution languages and are grounded with respect to simulated human-robot interaction transcripts.
ContributorsLumpkin, Barry Thomas (Author) / Baral, Chitta (Thesis advisor) / Lee, Joohyung (Committee member) / Fainekos, Georgios (Committee member) / Arizona State University (Publisher)
Created2012
Description
The present study examined the relations of children's effortful control (EC), emotion understanding, maladjustment, social competence, and relationship quality with nonparental caregivers in a sample of 30-, 42-, and 54-month olds. EC was measured with mothers' and caregivers' reports, as well as observed behavioral tasks. Emotion understanding was assessed by asking children to identify emotions during a puppet task. Mothers and caregivers also reported on children's problem behaviors and social competence. Caregivers provided reports of the quality of their relationship with children. Results from longitudinal structural equation models indicated that even after controlling for sex, SES, language ability, and previous levels of constructs, emotion understanding predicted EC one year later at 42 and 54 months. In addition, children with higher EC had more positive relationships with caregivers at 42 and 54 months. Although EC and EU were not significantly related to maladjustment and social competence after accounting for within time covariation among constructs and longitudinal stability, marginal findings were in expected directions and suggested that more regulated children with better emotion understanding skills had fewer behavioral problems and were more socially skilled. Findings are discussed in terms of the strengths and limitations of the present study.
ContributorsSilva, Kassondra M (Author) / Spinrad, Tracy L. (Thesis advisor) / Eisenberg, Nancy (Committee member) / Valiente, Carlos (Committee member) / Arizona State University (Publisher)
Created2012
Description
Natural Language Processing is a subject that combines computer science and linguistics, aiming to provide computers with the ability to understand natural language and to develop a more intuitive human-computer interaction. The research community has developed ways to translate natural language to mathematical formalisms. It has not yet been shown, however, how to automatically translate different kinds of knowledge in English to distinct formal languages. Most of the recent work presents the problem that the translation method aims to a specific formal language or is hard to generalize. In this research, I take a first step to overcome this difficulty and present two algorithms which take as input two lambda-calculus expressions G and H and compute a lambda-calculus expression F. The expression F returned by the first algorithm satisfies F@G=H and, in the case of the second algorithm, we obtain G@F=H. The lambda expressions represent the meanings of words and sentences. For each formal language that one desires to use with the algorithms, the language must be defined in terms of lambda calculus. Also, some additional concepts must be included. After doing this, given a sentence, its representation and knowing the representation of several words in the sentence, the algorithms can be used to obtain the representation of the other words in that sentence. In this work, I define two languages and show examples of their use with the algorithms. The algorithms are illustrated along with soundness and completeness proofs, the latter with respect to typed lambda-calculus formulas up to the second order. These algorithms are a core part of a natural language semantics system that translates sentences from English to formulas in different formal languages.
ContributorsAlvarez Gonzalez, Marcos (Author) / Baral, Chitta (Thesis advisor) / Lee, Joohyung (Committee member) / Ye, Jieping (Committee member) / Arizona State University (Publisher)
Created2010
Description
Previous research has suggested that the social interactions parents engage in with their typically developing children are critical to the relationships children form with peers later in development. Fewer studies, however, have investigated the relation between parent and child interactions and peer relations in children with autism. The current study aimed to investigate the relation between parent-child joint attention skills, social competence and friendship quality in children with autism and in typically developing children. A matched sample of 20 preschool-aged children with autism and 20 preschool-aged typically developing children were observed interacting with their parents in a laboratory setting. Approximately one year later, parents filled out a questionnaire assessing their child's social competency and quality of friendships with peers. Results indicated significant group differences between children with autism and typically developing children in all study variables, with children with autism displaying less initiation of joint attention, lower social competence and low quality friendships. Additionally, child initiated joint attention was positively related to social competence for both groups; effects were not moderated by diagnosis status. It is concluded that parent and child interactions during the preschool years are important to the development of social competence with peers. Intervention and policy implications are discussed.
ContributorsMeek, Shantel Elizabeth (Author) / Jahromi, Laudan (Thesis advisor) / Valiente, Carlos (Committee member) / Guimond, Amy (Committee member) / Arizona State University (Publisher)
Created2010
Description
Machine learning models can pick up biases and spurious correlations from training data and projects and amplify these biases during inference, thus posing significant challenges in real-world settings. One approach to mitigating this is a class of methods that can identify filter out bias-inducing samples from the training datasets to force models to avoid being exposed to biases. However, the filtering leads to a considerable wastage of resources as most of the dataset created is discarded as biased. This work deals with avoiding the wastage of resources by identifying and quantifying the biases. I further elaborate on the implications of dataset filtering on robustness (to adversarial attacks) and generalization (to out-of-distribution samples). The findings suggest that while dataset filtering does help to improve OOD(Out-Of-Distribution) generalization, it has a significant negative impact on robustness to adversarial attacks. It also shows that transforming bias-inducing samples into adversarial samples (instead of eliminating them from the dataset) can significantly boost robustness without sacrificing generalization.
ContributorsSachdeva, Bhavdeep Singh (Author) / Baral, Chitta (Thesis advisor) / Liu, Huan (Committee member) / Yang, Yezhou (Committee member) / Arizona State University (Publisher)
Created2021