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Articles 1 - 30 of 347

Full-Text Articles in Artificial Intelligence and Robotics

Information Extraction Tool Text2alm: From Narratives To Action Language System Descriptions, Craig Olson, Yuliya Lierler Aug 2019

Information Extraction Tool Text2alm: From Narratives To Action Language System Descriptions, Craig Olson, Yuliya Lierler

Yuliya Lierler

In this work we design a narrative understanding tool Text2Alm. System Text2Alm uses an action language ALM to perform inferences on complex interactions of events described in narratives. The methodology used to implement the Text2Alm was originally outlined by Lierler et al. 2017 via a manual process of converting a narrative to an ALM model. It relies on a conglomeration of resources and techniques from two distinct fields of artificial intelligence, namely, natural language processing and knowledge representation and reasoning. The effectiveness of system Text2Alm  is measured by its ability to correctly answer questions from the bAbI tasks published by …

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Could A Robot Be District Attorney?, Stephen E. Henderson Jun 2019

Could A Robot Be District Attorney?, Stephen E. Henderson

Stephen E Henderson

No abstract provided.

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Integrating Mathematics And Educational Robotics: Simple Motion Planning, Ronald I. Greenberg, George K. Thiruvathukal, Sara T. Greenberg Apr 2019

Integrating Mathematics And Educational Robotics: Simple Motion Planning, Ronald I. Greenberg, George K. Thiruvathukal, Sara T. Greenberg

George K. Thiruvathukal

This paper shows how students can be guided to integrate elementary mathematical analyses with motion planning for typical educational robots. Rather than using calculus as in comprehensive works on motion planning, we show students can achieve interesting results using just simple linear regression tools and trigonometric analyses. Experiments with one robotics platform show that use of these tools can lead to passable navigation through dead reckoning even if students have limited experience with use of sensors, programming, and mathematics.

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Automatic Program Rewriting In Non-Ground Answer Set Programs, Yuliya Lierler Jan 2019

Automatic Program Rewriting In Non-Ground Answer Set Programs, Yuliya Lierler

Yuliya Lierler

No abstract provided.

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Automatic Program Rewriting In Non-Ground Answer Set Programs, Nicholas Hippen, Yuliya Lierler Dec 2018

Automatic Program Rewriting In Non-Ground Answer Set Programs, Nicholas Hippen, Yuliya Lierler

Yuliya Lierler

Answer set programming is a popular constraint programming paradigm that has seen wide use across various industry applications. However, logic programs under answer set semantics often require careful design and nontrivial expertise from a programmer to obtain satisfactory solving times. In order to reduce this burden on a software engineer we propose an automated rewriting technique for non-ground logic programs that we implement in a system Projector. We conduct rigorous experimental analysis, which shows that applying system Projector to a logic program can improve its performance, even after significant human-performed optimizations.

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Strong Equivalence And Program's Structure In Arguing Essential Equivalence Between First-Order Logic Programs, Yuliya Lierler Dec 2018

Strong Equivalence And Program's Structure In Arguing Essential Equivalence Between First-Order Logic Programs, Yuliya Lierler

Yuliya Lierler

Answer set programming  is a prominent declarative programming paradigm used in formulating combinatorial search problems and implementing distinct knowledge representation formalisms. It is common that several related and yet substantially different answer set programs exist for a given problem. Sometimes these encodings may display significantly different performance. Uncovering precise formal links between these programs is often important and yet far from trivial. This paper claims the correctness   of a number of interesting program rewritings. Notably, they  assume  programs with variables and  such important language features as choice, disjunction, and aggregates. We showcase the utility of some considered rewritings  by using …

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Identification And Parasocial Relationships With Characters From Star Wars: The Force Awakens., Alice E. Hall Dec 2018

Identification And Parasocial Relationships With Characters From Star Wars: The Force Awakens., Alice E. Hall

Alice Hall

This study investigated identification and parasocial relationships (PSRs) with media characters by examining viewers’ responses to the movie Star Wars: The Force Awakens through an online survey of 113 audience members who saw the film in a theater within a month of its release. Participants reported stronger PSR and identification with the more familiar characters from the first trilogy than with the new characters introduced in the film, although the association with identification was limited to older participants. Star Wars fanship was associated with identification and PSR for old and new characters. Familiarity with the earlier films was associated with …

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Cross-Referencing Social Media And Public Surveillance Camera Data For Disaster Response, Chittayong Surakitbanharn,, Calvin Yau, Guizhen Wang, Aniesh Chawla, Yinuo Pan, Zhaoya Sun, Sam Yellin, David Ebert, Yung-Hsiang Lu, George K. Thiruvathukal Oct 2018

Cross-Referencing Social Media And Public Surveillance Camera Data For Disaster Response, Chittayong Surakitbanharn,, Calvin Yau, Guizhen Wang, Aniesh Chawla, Yinuo Pan, Zhaoya Sun, Sam Yellin, David Ebert, Yung-Hsiang Lu, George K. Thiruvathukal

George K. Thiruvathukal

Physical media (like surveillance cameras) and social media (like Instagram and Twitter) may both be useful in attaining on-the-ground information during an emergency or disaster situation. However, the intersection and reliability of both surveillance cameras and social media during a natural disaster are not fully understood. To address this gap, we tested whether social media is of utility when physical surveillance cameras went off-line during Hurricane Irma in 2017. Specifically, we collected and compared geo-tagged Instagram and Twitter posts in the state of Florida during times and in areas where public surveillance cameras went off-line. We report social media content …

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Smt-Based Constraint Answer Set Solver Ezsmt+ For Non-Tight Programs, Da Shen, Yuliya Lierler Sep 2018

Smt-Based Constraint Answer Set Solver Ezsmt+ For Non-Tight Programs, Da Shen, Yuliya Lierler

Yuliya Lierler

Constraint answer set programming integrates answer set programming with constraint processing. System Ezsmt+ is a constraint answer set programming tool that utilizes satisfiability modulo theory solvers for search. The truly unique feature of ezsmt+ is its capability to process linear as well as nonlinear constraints simultaneously containing integer and real variables.

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Strong Equivalence And Conservative Extensions Hand In Hand For Arguing Correctness Of New Action Language C Formalization, Yuliya Lierler Aug 2018

Strong Equivalence And Conservative Extensions Hand In Hand For Arguing Correctness Of New Action Language C Formalization, Yuliya Lierler

Yuliya Lierler

Answer set programming  is a  declarative programming paradigm used in formulating combinatorial search problems and implementing distinct knowledge representation and reasoning formalisms. It is common that several related and yet substantially different answer set programs exist for a given problem. Uncovering precise formal links between these programs is often of value. This paper develops a methodology for establishing such links. This methodology relies on the notions of strong equivalence and conservative extensions and a body of earlier theoretical work related to these concepts. We use distinct answer set programming formalizations  of an action language C and a syntactically restricted action …

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Smt-Based Answer Set Solver Cmodels-Diff (System Description), Da Shen, Yuliya Lierler Jun 2018

Smt-Based Answer Set Solver Cmodels-Diff (System Description), Da Shen, Yuliya Lierler

Yuliya Lierler

Many answer set solvers utilize Satisfiability solvers for search. SMT solvers extend Satisfiability solvers. This paper presents the CMODELS-DIFF system that uses SMT solvers to find answer sets of a logic program. Its theoretical foundation is based on Niemala's characterization of answer sets of a logic program via so called level rankings. The comparative experimental analysis demonstrates that CMODELS-DIFF is a viable answer set solver.

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Computer Vision Evidence Supporting Craniometric Alignment Of Rat Brain Atlases To Streamline Expert-Guided, First-Order Migration Of Hypothalamic Spatial Datasets Related To Behavioral Control, Arshad M. Khan, Jose G. Perez, Claire E. Wells, Olac Fuentes Apr 2018

Computer Vision Evidence Supporting Craniometric Alignment Of Rat Brain Atlases To Streamline Expert-Guided, First-Order Migration Of Hypothalamic Spatial Datasets Related To Behavioral Control, Arshad M. Khan, Jose G. Perez, Claire E. Wells, Olac Fuentes

Arshad M. Khan, Ph.D.

The rat has arguably the most widely studied brain among all animals, with numerous reference atlases for rat brain having been published since 1946. For example, many neuroscientists have used the atlases of Paxinos and Watson (PW, first published in 1982) or Swanson (S, first published in 1992) as guides to probe or map specific rat brain structures and their connections. Despite nearly three decades of contemporaneous publication, no independent attempt has been made to establish a basic framework that allows data mapped in PW to be placed in register with S, or vice versa. …

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Artificial Intelligence And Role-Reversible Judgment, Stephen E. Henderson, Kiel Brennan-Marquez Dec 2017

Artificial Intelligence And Role-Reversible Judgment, Stephen E. Henderson, Kiel Brennan-Marquez

Stephen E Henderson

As intelligent machines begin more generally outperforming human experts, why should humans remain ‘in the loop’ of decision-making?  One common answer focuses on outcomes: relying on intuition and experience, humans are capable of identifying interpretive errors—sometimes disastrous errors—that elude machines.  Though plausible today, this argument will wear thin as technology evolves.

Here, we seek out sturdier ground: a defense of human judgment that focuses on the normative integrity of decision-making.  Specifically, we propose an account of democratic equality as ‘role-reversibility.’  In a democracy, those tasked with making decisions should be susceptible, reciprocally, to the impact of decisions; there ought to …

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Prediction Of Solid Oxide Fuel Cell Performance Using Artificial Neural Network, M. A. Rafe Biswas, Kamwana N. Mwara Oct 2017

Prediction Of Solid Oxide Fuel Cell Performance Using Artificial Neural Network, M. A. Rafe Biswas, Kamwana N. Mwara

M. A. Rafe Biswas

NASA’s Johnson Space Center has recently begun efforts to eventually integrate air-independent Solid Oxide Fuel Cell (SOFC) systems, with landers that can be propelled by LOX-CH4, for long duration missions. Using landers that utilize such propellants, provides the opportunity to use SOFCs as a power option, especially since they are able to process methane into a reactant through fuel reformation. Various lead-up activities, such as hardware testing and computational modelling, have been initiated to assist with this developmental effort.
One modeling approach, currently being explored to predict SOFC behavior, involves the usage of artificial neural networks (ANN). Since SOFC performance …

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Trust And Prior Experience In Human-Robot Interaction, Tracy L. Sanders, Keith R. Macarthur, William Volante, Gabriella M. Hancock, Thomas Macgillivray, William T. Shugars, Peter A. Hancock Sep 2017

Trust And Prior Experience In Human-Robot Interaction, Tracy L. Sanders, Keith R. Macarthur, William Volante, Gabriella M. Hancock, Thomas Macgillivray, William T. Shugars, Peter A. Hancock

Keith Reid MacArthur

This experiment explored the influence of users’ experience (prior interaction) with robots on their attitudes and trust toward robotic agents. Specifically, we hypothesized that prior experience would lead to 1) higher trust scores after viewing a robot complete a task, 2) smaller differences in trust scores when comparing a human and a robot completing the same task, and 3) more positive general attitudes towards robots. These hypotheses were supported although not all results achieved significant levels of differentiation. These findings confirm that prior experience plays an important role in both user trust and general attitude in human-robot interactions.

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Effects Of Anthropomorphism On Trust In Human-Robot Interaction, Keith R. Macarthur, William T. Shugars, Tracy L. Sanders, Peter A. Hancock Aug 2017

Effects Of Anthropomorphism On Trust In Human-Robot Interaction, Keith R. Macarthur, William T. Shugars, Tracy L. Sanders, Peter A. Hancock

Keith Reid MacArthur

Robots are being integrated into everyday use, making the evaluation of trust in human-robot interactions (HRI) important to ensure their acceptance and correct usage (Lee & See, 2004; Parasuraman & Riley, 1997). Goetz, Kiesler, and Powers (2003) found that participants preferred robots with an anthropomorphic appearance appropriate for the social context of the task. This preference for robots with human-like appearance may be indicative of increased levels of trust and therefore, the present research evaluates the effects of anthropomorphism on trust.
Eighteen participants (Mage = 34.22, SDage = 10.55, n = 8 male, n =10 female) with …

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Comparison Of Visual Datasets For Machine Learning, Kent Gauen, Ryan Dailey, John Laiman, Yuxiang Zi, Nirmal Asokan, Yung-Hsiang Lu, George K. Thiruvathukal, Mei-Ling Shyu, Shu-Ching Chen Jul 2017

Comparison Of Visual Datasets For Machine Learning, Kent Gauen, Ryan Dailey, John Laiman, Yuxiang Zi, Nirmal Asokan, Yung-Hsiang Lu, George K. Thiruvathukal, Mei-Ling Shyu, Shu-Ching Chen

George K. Thiruvathukal

One of the greatest technological improvements in recent years is the rapid progress using machine learning for processing visual data. Among all factors that contribute to this development, datasets with labels play crucial roles. Several datasets are widely reused for investigating and analyzing different solutions in machine learning. Many systems, such as autonomous vehicles, rely on components using machine learning for recognizing objects. This paper compares different visual datasets and frameworks for machine learning. The comparison is both qualitative and quantitative and investigates object detection labels with respect to size, location, and contextual information. This paper also presents a new …

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Deep Learning Methods For Protein Torsion Angle Prediction, Haiou Li, Jie Hou, Badri Adhikari, Qiang Lyu, Jianlin Cheng Jan 2017

Deep Learning Methods For Protein Torsion Angle Prediction, Haiou Li, Jie Hou, Badri Adhikari, Qiang Lyu, Jianlin Cheng

Badri Adhikari

No abstract provided.

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First-Order Modular Logic Programs And Their Conservative Extensions (Extended Abstract), Amelia Harrison, Yuliya Lierler Dec 2016

First-Order Modular Logic Programs And Their Conservative Extensions (Extended Abstract), Amelia Harrison, Yuliya Lierler

Yuliya Lierler

This paper introduces first-order modular logic programs, which  provide a way of viewing answer set  programs  as consisting of many independent, meaningful modules. We also present conservative extensions of such programs. This concept helps to identify strong relationships between modular programs as well as between traditional programs. For example, we illustrate how the notion of a conservative extension can be used to justify the common projection rewriting. This is a short version of a paper that appeared at the 32nd International Conference on Logic Programming. 

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Action Languages And Question Answering, Yuliya Lierler, Daniela Inclezan, Michael Gelfond Dec 2016

Action Languages And Question Answering, Yuliya Lierler, Daniela Inclezan, Michael Gelfond

Yuliya Lierler

This paper describes a methodology for designing Question Answering  systems that utilize an action language ALM to allow inferences based on complex interactions of events described in texts. This methodology assumes the extension of the VERBNET lexicon with interpretable semantic annotations in ALM and specifies the use of several other NLP resources to produce ALM system descriptions for input discourses.

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What Is Answer Set Programming To Propositional Satisfiability, Yuliya Lierler Nov 2016

What Is Answer Set Programming To Propositional Satisfiability, Yuliya Lierler

Yuliya Lierler

Propositional satisfiability  (or satisfiability) and answer set programming are two closely related subareas of Artificial Intelligence that are used to model and solve difficult combinatorial search problems. Satisfiability solvers and answer set solvers  are the software systems that  find  satisfying interpretations and answer sets for given propositional formulas and logic programs, respectively. These systems are closely related in their common design patterns. In satisfiability, a propositional formula is used to encode problem specifications in a way that its satisfying interpretations correspond to the solutions of the problem. To find solutions to a problem it is then sufficient to use a …

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Perceptions Of Planned Versus Unplanned Malfunctions: A Human-Robot Interaction Scenario, Theresa T. Kessler, Keith R. Macarthur, Manuel Trujillo-Silva, Thomas Macgillivray, Chris Ripa, Peter A. Hancock Nov 2016

Perceptions Of Planned Versus Unplanned Malfunctions: A Human-Robot Interaction Scenario, Theresa T. Kessler, Keith R. Macarthur, Manuel Trujillo-Silva, Thomas Macgillivray, Chris Ripa, Peter A. Hancock

Keith Reid MacArthur

The present study investigated the effect of malfunctions on trust in a human-robot interaction scenario. Participants were exposed to either a planned or unplanned robot malfunction and then completed two different self-report trust measures. Resulting trust between planned and unplanned exposures was analyzed, showing that trust levels impacted by planned malfunctions did not significantly differ from those impacted by unplanned malfunctions. Therefore, it can be surmised that the methods used for the manipulation of the planned malfunctions were effective and are recommended for further study use.

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Gaussian Nonlinear Line Attractor For Learning Multidimensional Data, Theus H. Aspiras, Vijayan K. Asari, Wesam Sakla Oct 2016

Gaussian Nonlinear Line Attractor For Learning Multidimensional Data, Theus H. Aspiras, Vijayan K. Asari, Wesam Sakla

Vijayan K. Asari

The human brain’s ability to extract information from multidimensional data modeled by the Nonlinear Line Attractor (NLA), where nodes are connected by polynomial weight sets. Neuron connections in this architecture assumes complete connectivity with all other neurons, thus creating a huge web of connections. We envision that each neuron should be connected to a group of surrounding neurons with weighted connection strengths that reduces with proximity to the neuron. To develop the weighted NLA architecture, we use a Gaussian weighting strategy to model the proximity, which will also reduce the computation times significantly. Once all data has been trained in …

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Brain Machine Interface Using Emotiv Epoc To Control Robai Cyton Robotic Arm, Daniel P. Prince, Mark J. Edmonds, Andrew J. Sutter, Matthew Thomas Cusumano, Wenjie Lu, Vijayan K. Asari Oct 2016

Brain Machine Interface Using Emotiv Epoc To Control Robai Cyton Robotic Arm, Daniel P. Prince, Mark J. Edmonds, Andrew J. Sutter, Matthew Thomas Cusumano, Wenjie Lu, Vijayan K. Asari

Vijayan K. Asari

The initial framework for an electroencephalography (EEG) thought recognition software suite is developed, built, and tested. This suite is designed to recognize human thoughts and pair them to actions for controlling a robotic arm. Raw EEG brain activity data is collected using an Emotiv EPOC headset. The EEG data is processed through linear discriminant analysis (LDA), where an intended action is identified. The EEG classification suite is being developed to increase the number of distinct actions that can be identified compared to the Emotiv recognition software. The EEG classifier was able to correctly distinguish between two separate physical movements. Future …

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Constraint Cnf: A Sat And Csp Language Under One Roof, Broes De Cat, Yuliya Lierler Sep 2016

Constraint Cnf: A Sat And Csp Language Under One Roof, Broes De Cat, Yuliya Lierler

Yuliya Lierler

A new language, called constraint CNF, is proposed. It integrates propositional logic with constraints stemming from constraint programming (CP). A family of algorithms is designed to solve problems expressed in constraint CNF. These algorithms build on techniques from both propositional satisfiability (SAT) and CP. The result is a uniform language and an algorithmic framework, which allow us to gain a deeper understanding of the relation between the solving techniques used in SAT and in CP and apply them together.

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Constraint Answer Set Programming Versus Satisfiability Modulo Theories, Yuliya Lierler, Benjamin Susman Jun 2016

Constraint Answer Set Programming Versus Satisfiability Modulo Theories, Yuliya Lierler, Benjamin Susman

Yuliya Lierler

Constraint answer set programming is a promising research direction that integrates answer set programming with constraint processing. It is often informally related to the field of Satisfiability Modulo Theories. Yet, the exact formal link is obscured as the terminology and concepts used in these two research areas differ. In this paper, we make the link between these two areas precise.

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A Tool For Staging Mixed-Initiative Dialogs, Joshua W. Buck, Saverio Perugini Apr 2016

A Tool For Staging Mixed-Initiative Dialogs, Joshua W. Buck, Saverio Perugini

Saverio Perugini

We discuss and demonstrate a tool for prototyping dialog-based systems that, given a high-level specification of a human-computer dialog, stages the dialog for interactive use. The tool enables a dialog designer to evaluate a variety of dialogs without having to program each individual dialog, and serves as a proof-of-concept for our approach to mixed-initiative dialog modeling and implementation from a programming language-based perspective.

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Human-Robot Versus Human-Human Relationship Impact On Comfort Levels Regarding In Home Privacy, Keith R. Macarthur, Thomas G. Macgillivray, Eva L. Parkhurst, Peter A. Hancock Mar 2016

Human-Robot Versus Human-Human Relationship Impact On Comfort Levels Regarding In Home Privacy, Keith R. Macarthur, Thomas G. Macgillivray, Eva L. Parkhurst, Peter A. Hancock

Keith Reid MacArthur

When considering in-group vs. out-group concepts, certain degrees of human relationships naturally assume one of two categories. Roles such as immediate and extended family members and friends tend to fit quite nicely in the in-group category. Strangers, hired help, as well as acquaintances would likely be members of the out-group category due to a lack of personal relation to the perceiver. Though an out-group member may possess cultural, socioeconomic, or religious traits that an individual may perceive as in-group, the fact that they are an unknown stranger should immediately place them in the out-group. From [K1] this notion, it can be inferred …

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Intelligent Systems Development In A Non Engineering Curriculum, Emily Brand, William Honig, Matthew Wojtowicz Feb 2016

Intelligent Systems Development In A Non Engineering Curriculum, Emily Brand, William Honig, Matthew Wojtowicz

William L Honig

Much of computer system development today is programming in the large - systems of millions of lines of code distributed across servers and the web. At the same time, microcontrollers have also become pervasive in everyday products, economical to manufacture, and represent a different level of learning about system development. Real world systems at this level require integrated development of custom hardware and software.

How can academic institutions give students a view of this other extreme - programming on small microcontrollers with specialized hardware? Full scale system development including custom hardware and software is expensive, beyond the range of any …

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Universal Memory Architectures For Autonomous Machines, Dan Guralnik, Daniel E. Koditschek Dec 2015

Universal Memory Architectures For Autonomous Machines, Dan Guralnik, Daniel E. Koditschek

Dan Guralnik

We propose a self-organizing memory architecture (UMA) for perceptual experience provably capable of supporting autonomous learning and goal-directed problem solving in the absence of any prior information about the agent’s environment. The architecture is simple enough to ensure (1) a quadratic bound (in the number of available sensors) on space requirements, and (2) a quadratic bound on the time-complexity of the update-execute cycle. At the same time, it is sufficiently complex to provide the agent with an internal representation which is (3) minimal among all representations which account for every sensory equivalence class consistent with the agent’s belief state; (4) …

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