Artificial Intelligence and Robotics Commons^™

What Is Answer Set Programming To Propositional Satisfiability, Yuliya Lierler

Yuliya Lierler

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

Yuliya Lierler

Detection Of Diabetic Foot Ulcers Using Svm Based Classification, Lei Wang, Peder Pedersen, Diane Strong, Bengisu Tulu, Emmanuel Agu, Qian He, Ronald Ignotz, Raymond Dunn, David Harlan, Sherry Pagoto

Emmanuel O. Agu

Diabetic foot ulcers represent a significant health issue, for both patients’ quality of life and healthcare system costs. Currently, wound care is mainly based on visual assessment of wound size, which suffers from lack of accuracy and consistency. Hence, a more quantitative and computer-based method is needed. Supervised machine learning based object recognition is an attractive option, using training sample images with boundaries labeled by experienced clinicians. We use forty sample images collected from the UMASS Wound Clinic by tracking 8 subjects over 6 months with a smartphone camera. To maintain a consistent imaging environment and facilitate the capture process …

Wambot: Simulation And Modelling Of A Team Of Autonomous Mobile Robots, Martin Masek, Frank Ophelders, Sushil Pangeni, Adrian Boeing, Thomas Braunl

Martin Masek

Simulation is an essential early evaluation tool for mobile robot research and development, and different stages of development have individual simulator needs. In this paper, we document details of two simulation tools that were developed for an entry into the MAGIC 2010 challenge, an autonomous ground vehicle competition. In developing the entry, simulators were used in two domains: problem analysis and solution testing. The problem analysis simulator was built using a commercial 3D game engine, whilst the simulator aimed at testing of the solution was built using a standard robotics library. By leveraging existing technologies appropriate for each domain, the …

Rough-Fuzzy Hybrid Approach For Identification Of Bio-Markers And Classification On Alzheimer's Disease Data, Changsu Lee, Chiou-Peng Lam, Martin Masek

Martin Masek

A new approach is proposed in this paper for identification of biomarkers and classification on Alzheimer's disease data by employing a rough-fuzzy hybrid approach called ARFIS (a framework for Adaptive TS-type Rough-Fuzzy Inference Systems). In this approach, the entropy-based discretization technique is employed first on the training data to generate clusters for each attribute with respect to the output information. The rough set-based feature reduction method is then utilized to reduce the number of features in a decision table obtained using the cluster information. Another rough set-based approach is employed for the generation of decision rules. After the construction and …

Theory Identity: A Machine-Learning Approach, Kai Larsen, Dirk Hovorka, Jevin West, James Birt, James Pfaff, Trevor Chambers, Zebula Sampedro, Nick Zager, Bruce Vanstone

Bruce Vanstone

Theory identity is a fundamental problem for researchers seeking to determine theory quality, create theory ontologies and taxonomies, or perform focused theory-specific reviews and meta-analyses. We demonstrate a novel machine-learning approach to theory identification based on citation data and article features. The multi-disciplinary ecosystem of articles which cite a theory's originating paper is created and refined into the network of papers predicted to contribute to, and thus identify, a specific theory. We provide a 'proof-of-concept' for a highly-cited theory. Implications for crossdisciplinary theory integration and the identification of theories for a rapidly expanding scientific literature are discussed.

Voting Rules As Error Correcting Codes, Nisarg Shah, Ariel Procaccia, Yair Zick

Yair Zick

No abstract provided.

Non-Myopic Negotiators See What's Best, Yair Zick, Yoram Bachrach, Ian Kash, Peter Key

Yair Zick

No abstract provided.

From Question Context To Answer Credibility: Modeling Semantic Structures For Question Answering Using Statistical Methods, Protima Banerjee, Hyoil Han

Hyoil Han

Within a Question Answering (QA) framework, Question Context plays a vital role. We define Question Context to be background knowledge that can be used to represent the user’s information need more completely than the terms in the query alone. This paper proposes a novel approach that uses statistical language modeling techniques to develop a semantic Question Context which we then incorporate into the Information Retrieval (IR) stage of QA. Our approach proposes an Aspect-Based Relevance Language Model as basis of the Question Context Model. This model proposes that the sparse vocabulary of a query can be supplemented with semantic information …

A Computationally Efficient System For High-Performance Multi-Document Summarization, Sean Sovine, Hyoil Han

Hyoil Han

We propose and develop a simple and efficient algorithm for generating extractive multi-document summaries and show that this algorithm exhibits state-of-the-art or near state-of-the-art performance on two Document Understanding Conference datasets and two Text Analysis Conference datasets. Our results show that algorithms using simple features and computationally efficient methods are competitive with much more complex methods for multi-document summarization (MDS). Given these findings, we believe that our summarization algorithm can be used as a baseline in future MDS evaluations. Further, evidence shows that our system is near the upper limit of performance for extractive MDS.

Language Modeling Approaches To Information Retrieval, Protima Banerjee, Hyoil Han

Hyoil Han

This article surveys recent research in the area of language modeling (sometimes called statistical language modeling) approaches to information retrieval. Language modeling is a formal probabilistic retrieval framework with roots in speech recognition and natural language processing. The underlying assumption of language modeling is that human language generation is a random process; the goal is to model that process via a generative statistical model. In this article, we discuss current research in the application of language modeling to information retrieval, the role of semantics in the language modeling framework, cluster-based language models, use of language modeling for XML retrieval and …

Recognition And Resolution Of 'Comprehension Uncertainty' In Ai, Sukanto Bhattacharya, Kuldeep Kumar

Kuldeep Kumar

Handling uncertainty is an important component of most intelligent behaviour – so uncertainty resolution is a key step in the design of an artificially intelligent decision system (Clark, 1990). Like other aspects of intelligent systems design, the aspect of uncertainty resolution is also typically sought to be handled by emulating natural intelligence (Halpern, 2003; Ball and Christensen, 2009). In this regard, a number of computational uncertainty resolution approaches have been proposed and tested by Artificial Intelligence (AI) researchers over the past several decades since birth of Al as a scientific discipline in early 1950s post- publication of Alan Turing's landmark …

Mindscapes And Landscapes: Hayek And Simon On Cognitive Extension, Leslie Marsh

Leslie Marsh

Hayek’s and Simon’s social externalism runs on a shared presupposition: mind is constrained in its computational capacity to detect, harvest, and assimilate “data” generated by the infinitely fine-grained and perpetually dynamic characteristic of experience in complex social environments. For Hayek, mind and sociality are co-evolved spontaneous orders, allowing little or no prospect of comprehensive explanation, trapped in a hermeneutically sealed, i.e. inescapably context bound, eco-system. For Simon, it is the simplicity of mind that is the bottleneck, overwhelmed by the ambient complexity of the environmental. Since on Simon’s account complexity is unidirectional, Simon is far more ebullient about the prospects …

Analog Vlsi Implementation Of Support Vector Machine Learning And Classification, Sheng-Yu Peng, Bradley Minch, Paul Hasler

Bradley Minch

We propose an analog VLSI approach to implementing the projection neural networks adapted for the supportvector machine with radial-basis kernel functions, which are realized by a proposed floating-gate bump circuit with the adjustable width. Other proposed circuits include simple current mirrors and log-domain Alters. Neither resistors nor amplifiers are employed. Therefore it is suitable for large-scale neural network implementations. We show the measurement results of the bump circuit and verify the resulting analog signal processing system on the transistor level by using a SPICE simulator. The same approach can also be applied to the support vectorregression. With these analog signal …

Stigmergy 3.0: From Ants To Economies, Leslie Marsh, Margery Doyle

Leslie Marsh

No abstract provided.

Hayek's Philosophical Psychology, Leslie Marsh

Leslie Marsh

Hayek's philosophical psychology as set out in his The Sensory Order (1952) has, for the most part, been neglected. Despite being lauded by computer scientist grandee Frank Rosenblatt and by Nobel prize-winning biologist Gerald Edelman, cognitive scientists -- with a few exceptions -- have yet to discover Hayek's philosophical psychology. On the other hand, social theorists, Hayek's traditional disciplinary constituency, have only recently begun to take note and examine the importance of psychology in the complete Hayek corpus. This volume brings together for the first time state-of-the-art contributions from neuroscientists and philosophers of mind as well as economists and social …

Ssrn As An Initial Revolution In Academic Knowledge Aggregation And Dissemination, David Bray, Sascha Vitzthum, Benn Konsynski

Sascha Vitzthum

Within this paper we consider our results of using the Social Science Research Network (SSRN) over a period of 18 months to distribute our working papers to the research community. Our experiences have been quite positive, with SSRN serving as a platform both to inform our colleagues about our research as well as inform us about related research (through email and telephoned conversations of colleagues who discovered our paper on SSRN). We then discuss potential future directions for SSRN to consider, and how SSRN might well represent an initial revolution in 21st century academic knowledge aggregation and dissemination. Our paper …

Towards Self-Organizing, Smart Business Networks: Let’S Create ‘Life’ From Inert Information, David Bray, Benn Konsynski

David A. Bray

We review three different theories that can inform how researchers can determine the performance of smart business networks, to include: (1) the Theory of Evolution, (2) the Knowledge-Based Theory of the Firm, and (3) research insights into computers and cognition. We suggest that each of these theories demonstrate that to be generally perceived as smart, an organism needs to be self-organizing, communicative, and tool-making. Consequentially, to determine the performance of a smart business network, we suggest that researchers need to determine the degree to which it is self-organizing, communicative, and tool-making. We then relate these findings to the Internet and …

A History Of Political Experience, Leslie Marsh

Leslie Marsh

This book survives superficial but fails deeper scrutiny. A facile, undiscerning criticism of Lectures in the History of Political Thought (LHPT) is that on Oakeshott’s own account these are lectures on a non-subject: ‘I cannot detect anything which could properly correspond to the expression “the history of political thought”’ (p. 32). This is an entirely typical Oakeshottian swipe – elegant and oblique – at the title of the lecture course he inherited from Harold Laski. If title and quotation sit awkwardly we should remember that Oakeshott never prepared the text for publication – a fortiori he did not prepare it …

Fuzzy Neural Network Models For Classification, Arun D. Kulkarni, Charles D. Cavanaugh

Arun Kulkarni

Solving Ill-Posed Problems With Artificial Neural Networks, Arun D. Kulkarni

Arun Kulkarni