Artificial Intelligence and Robotics Commons^™

Artificial Intelligence – I: Usability Studies In Haptic Systems, Muzafar Khan, Suziah Sulaiman, Abas M. Said, Muhammad Tahir

International Conference on Information and Communication Technologies

Haptic systems that deal with force and tactile feedback are widely used in different domains. Usability evaluation plays an important role to assess these systems and user's experience. Many usability evaluation studies for haptic systems have been reported but no effort is made to effectively summarize those works; thus, little is known on the extent in which the methods applied are useful for these systems. Literature survey is performed to find out the patterns related to different evaluation methods and haptic devices used in various domains. The survey findings indicate for a need of new usability methods that would be …

Artificial Intelligence – I: A Preliminary Framework For Human-Agent Communication In Electronic Negotiations, Moez Ur Rehman, Nosheen Riaz

International Conference on Information and Communication Technologies

Electronic negotiations are business negotiations conducted via electronic means using information and communications technologies (ICT). Two dominant types of electronic negotiation systems are automated negotiation systems for software agents and negotiation support systems (NSSs) for humans. However, the integration of two types for human-agent negotiations is an important task. In this paper, an extended communication model for human-agent business negotiations is presented. For this purpose, the underlying communication models of automated negotiations and NSSs are analyzed. The extended communication model is based on a common negotiation ontology which captures the negotiation agenda and paves the way for such hybrid communication, …

Finding Robust-Under-Risk Solutions For Flowshop Scheduling, Steven O. Kimbrough, Ann Kuo, Hoong Chuin Lau

Research Collection School Of Computing and Information Systems

We propose and explore, in the context of benchmark problems for flowshop scheduling, a risk-based concept of robustness for optimization problems. This risk-based concept is in distinction to, and complements, the uncertainty-based concept employed in the field known as robust optimization. Implementation of our concept requires problem solution methods that sample the solution space intelligently and that produce large numbers of distinct sample points. With these solutions to hand, their robustness scores are easily obtained and heuristically robust solutions found. We find evolutionary computation to be effective for this purpose on these problems.

Real-World Parameter Tuning Using Factorial Design With Parameter Decomposition, Aldy Gunawan, Hoong Chuin Lau, Elaine Wong

Research Collection School Of Computing and Information Systems

In this paper, we explore the idea of improving the efficiency of factorial design for parameter tuning of metaheuristics. In a standard full factorial design, the number of runs increases exponentially as the number of parameters. To reduce the parameter search space, one option is to first partition parameters into disjoint categories. While this may be done manually based on user guidance, an automated approach proposed in this paper is to apply a fractional factorial design to partition parameters based on their main effects where each partition is then tuned independently. With a careful choice of fractional design, our approach …

Message-Passing Algorithms For Quadratic Programming Formulations Of Map Estimation, Akshat Kumar, Shlomo Zilberstein

Research Collection School Of Computing and Information Systems

Computing maximum a posteriori (MAP) estimation in graphical models is an important inference problem with many applications. We present message-passing algorithms for quadratic programming (QP) formulations of MAP estimation for pairwise Markov random fields. In particular, we use the concave-convex procedure (CCCP) to obtain a locally optimal algorithm for the non-convex QP formulation. A similar technique is used to derive a globally convergent algorithm for the convex QP relaxation of MAP. We also show that a recently developed expectation-maximization (EM) algorithm for the QP formulation of MAP can be derived from the CCCP perspective. Experiments on synthetic and real-world problems …

Solution Pluralism And Metaheuristics, Steven O. Kimbrough, Ann Kuo, Hoong Chuin Lau, Frederic H. Murphy, David Harlan Wood

Research Collection School Of Computing and Information Systems

Solution pluralism is an approach to problem solving and deliberation. It employs a plurality of distinct solutions for a decision problem for aiding decision making. The concept is well established in existing practice, although perhaps not recognized as such. This paper: (1) presents the concept as a generalization of established practice, (2) briefly describes successful uses of the concept in practice, and (3) presents several areas that appear would benefit from application of the concept. Throughout, the role of metaheuristics in finding the pluralities of solutions is emphasized.

Learning Author’S Writing Pattern System By Automata, Qun Yu

Master's Projects

The purpose of the report is to document our project’s theory, implementation and test results. The project works on an automata-based learning system which models authors’ writing characters with automatons. Since there were pervious works done by Dr. T.Y. Lin and Ms. S.X. Zhang, we continue on ALERGIA algorithm analysis and initial common pattern study in this project. Although every author has his/her own writing style, such as sentence length and word frequency etc, there are always some similarities in writing style. We hypothesize that common strings fogged the expected test result, just like the noise in radio wave. This …

Scalable Multiagent Planning Using Probabilistic Inference, Akshat Kumar, Shlomo Zilberstein, Marc Toussaint

Research Collection School Of Computing and Information Systems

Multiagent planning has seen much progress with the development of formal models such as Dec-POMDPs. However, the complexity of these models -- NEXP-Complete even for two agents -- has limited scalability. We identify certain mild conditions that are sufficient to make multiagent planning amenable to a scalable approximation w.r.t. the number of agents. This is achieved by constructing a graphical model in which likelihood maximization is equivalent to plan optimization. Using the Expectation-Maximization framework for likelihood maximization, we show that the necessary inference can be decomposed into processes that often involve a small subset of agents, thereby facilitating scalability. We …

A Novel Methodology For Evaluating User Interfaces In Health Care, Luca Longo, Bridget Kane

Conference papers

A pilot study is reported to identify an improved method of evaluating digital user interfaces in health care. Experience and developments from the aviation industry and the NASA-TLX mental workload assessment tools are applied in conjunction with Nielsen heuristics for evaluating an Electronic Health Record System in an Irish hospital. The NASA-TLX performs subjective workload assessments on operators working with various human-computer systems. Results suggest that depending on the cognitive workload and the working context of users, the usability will differ for the same digital interface. We conclude that incorporating the NASA-TLX with Nielsen's heuristics offers a more reliable method …

Diagnosis Of Skin Diseases Using Online Expert System, Dr. Muhammad Zubair Asghar, Muhammad Junaid Asghar

Dr. Muhammad Zubair Asghar

This paper describes Expert System (ES) for diagnosis and management of skin diseases. More than 13 types of skin diseases can be diagnosed and treated by our system. It is rule based web-supported expert system, assisting skin specialists, medical students doing specialization in dermatology, researchers as well as skin patients having computer know-how. System was developed with Java Technology. The expert rules were developed on the symptoms of each type of skin disease, and they were presented using tree-graph and inferred using forward-chaining with depth-first search method. User interaction with system is enhanced with efficient user interfaces. The web based …

Simplified Neural Network Design For Hand Written Digit Recognition, Dr. Muhammad Zubair Asghar

Dr. Muhammad Zubair Asghar

In this work a very simple and flexible neural network scheme is proposed and implemented for handwritten digit recognition, which will assist beginners and A.I students who want to understand perceptive capability of neural network. In the proposed system, a very simple design of artificial neural networks is implemented. First of all learning mechanism of the neural network is described and then its architecture is discussed. Proposed network is trained in supervised manner using various (approx: 250) patterns /fonts of handwritten digits. Unique token is allocated to digit when it is made input to the system. Network becomes adaptive when …

Effects Of Appearance And Functions On Likability And Perceived Occupational Suitability Of Robots, Sau-Lai Lee, Ivy Yee-Man Lau, Ying-Yi Hong

Research Collection School of Social Sciences

This article reports three experiments that examined the association between (a) appearances and perceived capabilities of robots, (b) appearance and capabilities of robots and liking for the robots, and (c) perceived capabilities of robots and judgments concerning their suitability for different occupations. In Experiment 1, the authors found that participants perceived human- and animal-like robots to have relatively more warmth-related (e.g., emotion) capabilities than machinelike robots have. In Experiment 2, the authors found that liking for robots was not affected by their human likeness or their having warmth or competence capabilities. In Experiment 3, participants generally thought that robots should …

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

Computer Science: Faculty Publications and Other Works

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 …

Autonomous Robot Skill Acquisition, George D. Konidaris

Open Access Dissertations

Among the most impressive of aspects of human intelligence is skill acquisition—the ability to identify important behavioral components, retain them as skills, refine them through practice, and apply them in new task contexts. Skill acquisition underlies both our ability to choose to spend time and effort to specialize at particular tasks, and our ability to collect and exploit previous experience to become able to solve harder and harder problems over time with less and less cognitive effort.

Hierarchical reinforcement learning provides a theoretical basis for skill acquisition, including principled methods for learning new skills and deploying them during problem solving. …

Multiagent Coalition Formation In Uncertain Environments With Type-Changing Influences And Its Application Towards Forming Human Coalitions, Nobel A. Khandaker

Department of Computer Science and Engineering: Dissertations, Theses, and Student Research

We aim to solve the problem forming multiagent coalitions in uncertain environments where the coalition members’ capability of solving tasks change due to their learning. The MCFP-Mproblem for the agents refers to forming or joining coalitions on behalf of a set of human users so that those human users can solve tasks and improve their types (expertise) to improve their performances over time. MCFP-A problem for a set of agents refers to their forming or joining coalitions so that they are able to solve a set of assigned tasks while optimize their performance over time. We propose the Integrated Human …

Noise, Delays, And Resonance In A Neural Network, Austin Quan

HMC Senior Theses

A stochastic-delay differential equation (SDDE) model of a small neural network with recurrent inhibition is presented and analyzed. The model exhibits unexpected transient behavior: oscillations that occur at the boundary of the basins of attraction when the system is bistable. These are known as delay-induced transitory oscillations (DITOs). This behavior is analyzed in the context of stochastic resonance, an unintuitive, though widely researched phenomenon in physical bistable systems where noise can play in constructive role in strengthening an input signal. A method for modeling the dynamics using a probabilistic three-state model is proposed, and supported with numerical evidence. The potential …

Incremental Dcop Search Algorithms For Solving Dynamic Dcop Problems, William Yeoh, Pradeep Varakantham, Xiaoxun Sun, Sven Koenig

Research Collection School Of Computing and Information Systems

Distributed constraint optimization problems (DCOPs) are well-suited for modeling multi-agent coordination problems. However, most research has focused on developing algorithms for solving static DCOPs. In this paper, we model dynamic DCOPs as sequences of (static) DCOPs with changes from one DCOP to the next one in the sequence. We introduce the ReuseBounds procedure, which can be used by any-space ADOPT and any-space BnB-ADOPT to find cost-minimal solutions for all DCOPs in the sequence faster than by solving each DCOP individually. This procedure allows those agents that are guaranteed to remain unaffected by a change to reuse their lower and upper …

Adaptive Decision Support For Structured Organizations: A Case For Orgpomdps, Pradeep Reddy Varakantham, Nathan Schurr, Alan Carlin, Christopher Amato

Research Collection School Of Computing and Information Systems

In today's world, organizations are faced with increasingly large and complex problems that require decision-making under uncertainty. Current methods for optimizing such decisions fall short of handling the problem scale and time constraints. We argue that this is due to existing methods not exploiting the inherent structure of the organizations which solve these problems. We propose a new model called the OrgPOMDP (Organizational POMDP), which is based on the partially observable Markov decision process (POMDP). This new model combines two powerful representations for modeling large scale problems: hierarchical modeling and factored representations. In this paper we make three key contributions: …

Message-Passing Algorithms For Large Structured Decentralized Pomdps, Akshat Kumar, Shlomo Zilberstein

Research Collection School Of Computing and Information Systems

Decentralized POMDPs provide a rigorous framework for multi-agent decision-theoretic planning. However, their high complexity has limited scalability. In this work, we present a promising new class of algorithms based on probabilistic inference for infinite-horizon ND-POMDPs---a restricted Dec-POMDP model. We first transform the policy optimization problem to that of likelihood maximization in a mixture of dynamic Bayes nets (DBNs). We then develop the Expectation-Maximization (EM) algorithm for maximizing the likelihood in this representation. The EM algorithm for ND-POMDPs lends itself naturally to a simple message-passing paradigm guided by the agent interaction graph. It is thus highly scalable w.r.t. the number of …

A Simple Curious Agent To Help People Be Curious, Han Yu, Zhiqi Shen, Chunyan Miao, Ah-Hwee Tan

Research Collection School Of Computing and Information Systems

Curiosity is an innately rewarding state of mind that, over the millennia, has driven the human race to explore and discover. Many researches in pedagogical science have confirmed the importance of being curious to the students' cognitive development. However, in the newly popular virtual world-based learning environments (VLEs), there is currently a lack of attention being paid to enhancing the learning experience by stimulating the learners' curiosity. In this paper, we propose a simple model for curious agents (CAs) which can be used to stimulate learners' curiosity in VLEs. Potential future research directions will be discussed.