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Articles 10891 - 10920 of 11085

Full-Text Articles in Artificial Intelligence and Robotics

Enhancing Undergraduate Ai Courses Through Machine Learning Projects, Ingrid Russell, Zdravko Markov, Todd W. Neller, Susan Coleman Oct 2005

Enhancing Undergraduate Ai Courses Through Machine Learning Projects, Ingrid Russell, Zdravko Markov, Todd W. Neller, Susan Coleman

Computer Science Faculty Publications

It is generally recognized that an undergraduate introductory Artificial Intelligence course is challenging to teach. This is, in part, due to the diverse and seemingly disconnected core topics that are typically covered. The paper presents work funded by the National Science Foundation to address this problem and to enhance the student learning experience in the course. Our work involves the development of an adaptable framework for the presentation of core AI topics through a unifying theme of machine learning. A suite of hands-on semester-long projects are developed, each involving the design and implementation of a learning system that enhances a …

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Cooperative Reinforcement Learning Using An Expert-Measuring Weighted Strategy With Wolf, Kevin Cousin, Gilbert L. Peterson Sep 2005

Cooperative Reinforcement Learning Using An Expert-Measuring Weighted Strategy With Wolf, Kevin Cousin, Gilbert L. Peterson

Faculty Publications

Gradient descent learning algorithms have proven effective in solving mixed strategy games. The policy hill climbing (PHC) variants of WoLF (Win or Learn Fast) and PDWoLF (Policy Dynamics based WoLF) have both shown rapid convergence to equilibrium solutions by increasing the accuracy of their gradient parameters over standard Q-learning. Likewise, cooperative learning techniques using weighted strategy sharing (WSS) and expertness measurements improve agent performance when multiple agents are solving a common goal. By combining these cooperative techniques with fast gradient descent learning, an agent’s performance converges to a solution at an even faster rate. This statement is verified in a …

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Solving Generalized Open Constraint Optimization Problem Using Two-Level Multi-Agent Framework, Hoong Chuin Lau, Lei Zhang, Chang Liu Sep 2005

Solving Generalized Open Constraint Optimization Problem Using Two-Level Multi-Agent Framework, Hoong Chuin Lau, Lei Zhang, Chang Liu

Research Collection School Of Computing and Information Systems

The Open Constraint Optimization Problem (OCOP) refers to the COP where constraints and variable domains can change over time and agents' opinions have to be sought over a distributed network to form a solution. The openness of the problem has caused conventional approaches to COP such as branch-and-bound to fail to find optimal solutions. OCOP is a new problem and the approach to find an optimal solution (minimum total cost) introduced in [1] is based on an unrealistic assumption that agents are willing to report their options in nondecreasing order of cost. In this paper, we study a generalized OCOP …

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Service-Oriented E-Learning Architecture Using Web Service-Based Intelligent Agents, Nasir Hussain, M. Khalid Khan Aug 2005

Service-Oriented E-Learning Architecture Using Web Service-Based Intelligent Agents, Nasir Hussain, M. Khalid Khan

International Conference on Information and Communication Technologies

There is no doubt that e-learning has found its way in our lives. From the very start to the Ph.D. level one can find e-learning courses every where and all the big names are supporting it. One thing that is needed to be understood is that e-learning is basically the integration of various technologies. Now this technology is maturing and we can find different standards for e-learning .New technologies such as agents and web services are promising better results. In this paper we have proposed an e-learning architecture that is dependent on multi-agent systems and web services. These communication technologies …

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Poster Session A: Fingerprint Matching Using Ridge Patterns, Muhammad Umer Munir, Dr. Muhammad Younus Javed Aug 2005

Poster Session A: Fingerprint Matching Using Ridge Patterns, Muhammad Umer Munir, Dr. Muhammad Younus Javed

International Conference on Information and Communication Technologies

This paper presents a fingerprint matching scheme that utilizes a ridge patterns to match fingerprint images. The proposed scheme uses a set of 16 Gabor filters where spatial frequencies correspond to the average inter-ridge spacing in fingerprints. It is used to capture the ridge strength at equally spaced orientations. A circular tessellation of filtered image is then used to construct the ridge feature map. This ridge feature map contains both global and local details in a fingerprint as a compact fixed length feature vector. The fingerprint matching is based on the Euclidean distance between two corresponding feature vectors. The genuine …

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Poster Session A: Face Recognition Using Sub-Holistic Pca, Muhammad Murtaza Khan, Dr. Muhammad Younus Javed, Muhammad Almas Anjum Aug 2005

Poster Session A: Face Recognition Using Sub-Holistic Pca, Muhammad Murtaza Khan, Dr. Muhammad Younus Javed, Muhammad Almas Anjum

International Conference on Information and Communication Technologies

This paper proposes a face recognition scheme that enhances the correct face recognition rate as compared to conventional Principal Component Analysis (PCA). The proposed scheme, Sub-Holistic PCA (SH-PCA), was tested using ORL database and out performed PCA for all test scenarios. SH-PCA requires more computational power and memory as compared to PCA however it yields an improvement of 6% correct recognition on the complete ORL database of 400 images. The correct recognition rate for the complete ORL database is 90% for the SH-PCA technique.

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Evaluation Of Time-Varying Availability In Multi-Echelon Inventory System With Combat Damage, Hoong Chuin Lau, Huawei Song Aug 2005

Evaluation Of Time-Varying Availability In Multi-Echelon Inventory System With Combat Damage, Hoong Chuin Lau, Huawei Song

Research Collection School Of Computing and Information Systems

The models for multi-echelon inventory systems in existing literatures predominantly address failures due to reliability in peacetime. In wartime or even peacetime operational scenarios, unexpected combat damage can cause a large number of systems to be heavily damaged, to the extent that they become irreparable. In this paper, we study a multi-echelon spare parts support system under combat damage, discuss the replenishment policy and propose an approximate method to evaluate the time-varying system performance operational availability considering the effect of passivation. Experiments show our model works well and efficiently against simulation.

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Tuning Tabu Search Strategies Via Visual Diagnosis, Steven Halim, Wee Chong Wan, Hoong Chuin Lau Aug 2005

Tuning Tabu Search Strategies Via Visual Diagnosis, Steven Halim, Wee Chong Wan, Hoong Chuin Lau

Research Collection School Of Computing and Information Systems

While designing working metaheuristics can be straightforward, tuning them to solve the underlying combinatorial optimization problem well can be tricky. Several tuning methods have been proposed but they do not address the new aspect of our proposed classification of the metaheuristic tuning problem: tuning search strategies. We propose a tuning methodology based on Visual Diagnosis and a generic tool called Visualizer for Metaheuristics Development Framework(V-MDF) to address specifically the problem of tuning search (particularly Tabu Search) strategies. Under V-MDF, we propose the use of a Distance Radar visualizer where the human and computer can collaborate to diagnose the occurrence of …

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Exploiting Belief Bounds: Practical Pomdps For Personal Assistant Agents, Pradeep Varakantham, Rajiv Maheswaran, Milind Tambe Jul 2005

Exploiting Belief Bounds: Practical Pomdps For Personal Assistant Agents, Pradeep Varakantham, Rajiv Maheswaran, Milind Tambe

Research Collection School Of Computing and Information Systems

Agents or agent teams deployed to assist humans often face the challenges of monitoring the state of key processes in their environment (including the state of their human users themselves) and making periodic decisions based on such monitoring. POMDPs appear well suited to enable agents to address these challenges, given the uncertain environment and cost of actions, but optimal policy generation for POMDPs is computationally expensive. This paper introduces three key techniques to speedup POMDP policy generation that exploit the notion of progress or dynamics in personal assistant domains. Policy computation is restricted to the belief space polytope that remains …

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Approximate Strategic Reasoning Through Hierarchical Reduction Of Large Symmetric Games, Michael P. Wellman, Daniel M. Reeves, Kevin M. Lochner, Shih-Fen Cheng, Rahul Suri Jul 2005

Approximate Strategic Reasoning Through Hierarchical Reduction Of Large Symmetric Games, Michael P. Wellman, Daniel M. Reeves, Kevin M. Lochner, Shih-Fen Cheng, Rahul Suri

Research Collection School Of Computing and Information Systems

To deal with exponential growth in the size of a game with the number of agents, we propose an approximation based on a hierarchy of reduced games. The reduced game achieves savings by restricting the number of agents playing any strategy to fixed multiples. We validate the idea through experiments on randomly generated local-effect games. An extended application to strategic reasoning about a complex trading scenario motivates the approach, and demonstrates methods for game-theoretic reasoning over incompletely-specified games at multiple levels of granularity.

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Valuations Of Possible States (Vps): A Unifying Quantitative Framework For Evaluating Privacy In Collaboration, Rajiv T. Maheswaran, Jonathan Pearce, Pradeep Varakantham, Emma Bowring, Milind Tambe Jul 2005

Valuations Of Possible States (Vps): A Unifying Quantitative Framework For Evaluating Privacy In Collaboration, Rajiv T. Maheswaran, Jonathan Pearce, Pradeep Varakantham, Emma Bowring, Milind Tambe

Research Collection School Of Computing and Information Systems

For agents deployed in real-world settings, such as businesses, universities and research laboratories, it is critical that agents protect their individual users’ privacy when interacting with others entities. Indeed, privacy is recognized as a key motivating factor in design of several multiagent algorithms, such as distributed constraint optimization (DCOP) algorithms. Unfortunately, rigorous and general quantitative metrics for analysis and comparison of such multiagent algorithms with respect to privacy loss are lacking. This paper takes a key step towards developing a general quantitative model from which one can analyze and generate metrics of privacy loss by introducing the VPS (Valuations of …

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Evaluating Online Trust Using Machine Learning Methods, Weihua Song Apr 2005

Evaluating Online Trust Using Machine Learning Methods, Weihua Song

Doctoral Dissertations

Trust plays an important role in e-commerce, P2P networks, and information filtering. Current challenges in trust evaluations include: (1) fnding trustworthy recommenders, (2) aggregating heterogeneous trust recommendations of different trust standards based on correlated observations and different evaluation processes, and (3) managing efficiently large trust systems where users may be sparsely connected and have multiple local reputations. The purpose of this dissertation is to provide solutions to these three challenges by applying ordered depth-first search, neural network, and hidden Markov model techniques. It designs an opinion filtered recommendation trust model to derive personal trust from heterogeneous recommendations; develops a reputation …

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Walverine: A Walrasian Trading Agent, Shih-Fen Cheng, Evan Leung, Kevin M. Lochner, Kevin O'Malley, Daniel M. Reeves, Julian L. Schvartzman, Michael P. Wellman Apr 2005

Walverine: A Walrasian Trading Agent, Shih-Fen Cheng, Evan Leung, Kevin M. Lochner, Kevin O'Malley, Daniel M. Reeves, Julian L. Schvartzman, Michael P. Wellman

Research Collection School Of Computing and Information Systems

TAC-02 was the third in a series of Trading Agent Competition events fostering research in automating trading strategies by showcasing alternate approaches in an open-invitation market game. TAC presents a challenging travel-shopping scenario where agents must satisfy client preferences for complementary and substitutable goods by interacting through a variety of market types. Michigan's entry, Walverine, bases its decisions on a competitive (Walrasian) analysis of the TAC travel economy. Using this Walrasian model, we construct a decision-theoretic formulation of the optimal bidding problem, which Walverine solves in each round of bidding for each good. Walverine's optimal bidding approach, as well as …

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Rapid Prototyping For The Design Of Virtual Worlds, Prabhu V. Krishnan Apr 2005

Rapid Prototyping For The Design Of Virtual Worlds, Prabhu V. Krishnan

Electrical & Computer Engineering Theses & Dissertations

Development of Virtual Reality (VR) applications is challenging where application developers are required to have expertise in the target VR technologies along with the problem domain expertise. New VR technologies impose a significant learning curve to even the most experienced VR developer. The proposed solution relies on synthesis to automate the migration of a VR application to a new unfamiliar VR platform/technology. To solve the problem, the Common Scene Definition Framework (CSDF) was developed, that serves as a superset/model representation of the target virtual world. Input modules were developed to populate the framework with the capabilities of the virtual world …

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Unifying An Introduction To Artificial Intelligence Course Through Machine Learning Laboratory Experiences, Ingrid Russell, Zdravko Markov, Todd W. Neller, Michael Georgiopoulos, Susan Coleman Jan 2005

Unifying An Introduction To Artificial Intelligence Course Through Machine Learning Laboratory Experiences, Ingrid Russell, Zdravko Markov, Todd W. Neller, Michael Georgiopoulos, Susan Coleman

Computer Science Faculty Publications

This paper presents work on a collaborative project funded by the National Science Foundation that incorporates machine learning as a unifying theme to teach fundamental concepts typically covered in the introductory Artificial Intelligence courses. The project involves the development of an adaptable framework for the presentation of core AI topics. This is accomplished through the development, implementation, and testing of a suite of adaptable, hands-on laboratory projects that can be closely integrated into the AI course. Through the design and implementation of learning systems that enhance commonly-deployed applications, our model acknowledges that intelligent systems are best taught through their application …

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Terminator Or Super Mario: Human/Computer Hybrids, Actual And Virtual, Noreen L. Herzfeld Jan 2005

Terminator Or Super Mario: Human/Computer Hybrids, Actual And Virtual, Noreen L. Herzfeld

Theology Faculty Publications

Is a human/computer hybrid feasible: If so, in what ways would such hybridization affect our concept of what it means to be human? There are two forms of such hybridization, the actual and the virtual. Actual hybridization involves the implantation of mechanical devices in the human body. In actual hybridization the computer comes to us and to our body to enhance our functioning in our world. In virtual hybridization we go to the computer, projecting our minds into the world of cyberspace and being formed there. Perhaps the most common form of virtual hybridization is the immersion our children experience …

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Human Mental Models Of Humanoid Robots, Sau-Lai Lee, Ivy Yee-Man Lau, Sara Kiesler, Chi-Yue Chiu Jan 2005

Human Mental Models Of Humanoid Robots, Sau-Lai Lee, Ivy Yee-Man Lau, Sara Kiesler, Chi-Yue Chiu

Research Collection School of Social Sciences

Effective communication between a person and a robot may depend on whether there exists a common ground of understanding between the two. In two experiments modelled after human-human studies we examined how people form a mental model of a robot's factual knowledge. Participants estimated the robot's knowledge by extrapolating from their own knowledge and from information about the robot's origin and language. These results suggest that designers of humanoid robots must attend not only to the social cues that robots emit but also to the information people use to create mental models of a robot.

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A Context-Dependent Model Of Proximity In Physically Situated Environments, John D. Kelleher, Geert-Jan M. Kruijff Jan 2005

A Context-Dependent Model Of Proximity In Physically Situated Environments, John D. Kelleher, Geert-Jan M. Kruijff

Conference papers

The paper presents a computational model for a context-dependent analysis of a physical environment in terms of spatial proximity. The model provides a basis for grounding linguistic analyses of spatial expressions in visual perception. The model uses potential fields to model spatial proximity. It has been implemented, and when combined with a handcrafted grammar, is used to enable a conversational robot to carry out a situated dialogue with a human. The key concept in our approach is defining the region that is proximal to a landmark based on the spatial configuration of other objects in the scene. The model extends …

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An Assessment Of Case-Based Reasoning For Spam Filtering, Sarah Jane Delany, Padraig Cunningham, Lorcan Coyle Jan 2005

An Assessment Of Case-Based Reasoning For Spam Filtering, Sarah Jane Delany, Padraig Cunningham, Lorcan Coyle

Articles

Because of the changing nature of spam, a spam filtering system that uses machine learning will need to be dynamic. This suggests that a case-based (memory-based) approach may work well. Case-Based Reasoning (CBR) is a lazy approach to machine learning where induction is delayed to run time. This means that the case base can be updated continuously and new training data is immediately available to the induction process. In this paper we present a detailed description of such a system called ECUE and evaluate design decisions concerning the case representation. We compare its performance with an alternative system that uses …

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Robust Temporal Constraint Networks, Hoong Chuin Lau, Thomas Ou, Melvyn Sim Jan 2005

Robust Temporal Constraint Networks, Hoong Chuin Lau, Thomas Ou, Melvyn Sim

Research Collection School Of Computing and Information Systems

In this paper, we propose the Robust Temporal Constraint Network (RTCN) model for simple temporal constraint networks where activity durations are bounded by random variables. The problem is to determine whether such temporal network can be executed with failure probability less than a given 0 ≤ E ≤ 1 for each possible instantiation of the random variables, and if so. how one might find a feasible schedule with each given instantiation. The advantage of our model is that one can vary the value of ∊ to control the level of conservativeness of the solution. We present a computationally tractable and …

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Artificial Neural Networks : A Comparative Study Of Implementations For Human Chromosome Classification, Nancy Akl Jan 2005

Artificial Neural Networks : A Comparative Study Of Implementations For Human Chromosome Classification, Nancy Akl

Theses : Honours

Artificial neural networks are a popular field of artificial intelligence and have commonly been applied to solve many prediction, classification and diagnostic tasks. One such task is the analysis of human chromosomes. This thesis investigates the use of artificial neural networks (ANNs) as automated chromosome classifiers. The investigation involves the thorough analysis of seven different implementation techniques. These include three techniques using artificial neural networks, two techniques using ANN s supported by another method and two techniques not using ANNs. These seven implementations are evaluated according to the classification accuracy achieved and according to their support of important system measures, …

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A Multi-Agent Approach For Solving Optimization Problems Involving Expensive Resources, Hoong Chuin Lau, H. Wang Jan 2005

A Multi-Agent Approach For Solving Optimization Problems Involving Expensive Resources, Hoong Chuin Lau, H. Wang

Research Collection School Of Computing and Information Systems

In this paper, we propose a multi-agent approach for solving a class of optimization problems involving expensive resources, where monolithic local search schemes perform miserably. More specifically, we study the class of bin-packing problems. Under our proposed Fine-Grained Agent System scheme, rational agents work both collaboratively and selfishly based on local search and mimic physics-motivated systems. We apply our approach to a generalization of bin-packing - the Inventory Routing Problem with Time Windows - which is an important logistics problem, and demonstrate the efficiency and effectiveness of our approach.

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Justilm: Few-Shot Justification Generation For Explainable Fact-Checking Of Real-World Claims, Fengzhu Zeng, Wei Gao Dec 2004

Justilm: Few-Shot Justification Generation For Explainable Fact-Checking Of Real-World Claims, Fengzhu Zeng, Wei Gao

Research Collection School Of Computing and Information Systems

Justification is an explanation that supports the verdict assigned to a claim in fact-checking. However, the task of justification generation is previously oversimplified as summarization of fact-check article authored by professional checkers. In this work, we propose a realistic approach to generate justification based on retrieved evidence. We present a new benchmark dataset called ExClaim for Explainable Claim verification, and introduce JustiLM, a novel few-shot retrieval-augmented language model to learn justification generation by leveraging fact-check articles as auxiliary resource during training. Our results show that JustiLM outperforms in-context learning (ICL)-enabled LMs including Flan-T5 and Llama2, and the retrieval-augmented model Atlas …

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A Periodic-Review Inventory Model With Application To The Continuous-Review Obsolescence Problem, Yuyue Song, Hoong Chuin Lau Nov 2004

A Periodic-Review Inventory Model With Application To The Continuous-Review Obsolescence Problem, Yuyue Song, Hoong Chuin Lau

Research Collection School Of Computing and Information Systems

In this paper we consider a stochastic-demand periodic-review inventory model with sudden obsolescence. We characterize the structure of the optimal policy and propose a dynamic programming algorithm for computing its parameters. We then utilize this algorithm to approximate the solution to the continuous-review sudden obsolescence problem with general obsolescence distribution. We prove convergence of our approximation scheme, and demonstrate it numerically against known closed-form solutions of special cases.

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Two-Echelon Repairable Item Inventory System With Limited Repair Capacity Under Nonstationary Demands, Hoong Chuin Lau, Huawei Song Nov 2004

Two-Echelon Repairable Item Inventory System With Limited Repair Capacity Under Nonstationary Demands, Hoong Chuin Lau, Huawei Song

Research Collection School Of Computing and Information Systems

We study a repairable item inventory system under limited repair capacity and nonstationary Poisson demands, motivated by corrective maintenance of military equipment. Our goal is to minimize the cost of both spare and repair resource allocation. We propose an efficient analytical model that combines optimization modeling and queuing theory.

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Corrective Maintenance Optimization In An Air Force, Hoong Chuin Lau, K. Y. Neo, W. C. Wan Nov 2004

Corrective Maintenance Optimization In An Air Force, Hoong Chuin Lau, K. Y. Neo, W. C. Wan

Research Collection School Of Computing and Information Systems

Successful military mission planning and execution depend critically on equipment serviceability and resupply. Due to the stochastic nature of demands, the forecast of optimal spares and resources needed to guarantee the level of serviceability is a complex problem, especially in a multi-echelon setting. In this paper, we propose a decision-support concept and software tool known as Corrective Maintenance Optimizer (CMO) that helps to optimize system availability, through proper allocation of spare parts, both strategically and operationally.

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Job Scheduling With Unfixed Availability Constraints, Hoong Chuin Lau, C. Zhang Nov 2004

Job Scheduling With Unfixed Availability Constraints, Hoong Chuin Lau, C. Zhang

Research Collection School Of Computing and Information Systems

Standard scheduling theory assumes that all machines are continuously available throughout the planning horizon. In many manufacturing and service management situations however, machines need to be maintained periodically to prevent malfunctions. During the maintenance period, a machine is not available for processing jobs. Hence, a more realistic scheduling model should take into account machine maintenance activities. In this paper, we study the problem of job scheduling with unfixed availability constraints on a single machine. We first propose a preliminary classification for the scheduling problem with unfixed availability constraints based on maintenance constraints, job characteristics and objective function. We divide our …

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Logistics Network Design With Differentiated Delivery Lead Time: A Chemical Industry Case Study, Michelle Lee Fong Cheong, Rohit Bhatnagar, Stephen C. Graves Nov 2004

Logistics Network Design With Differentiated Delivery Lead Time: A Chemical Industry Case Study, Michelle Lee Fong Cheong, Rohit Bhatnagar, Stephen C. Graves

Research Collection School Of Computing and Information Systems

Most logistics network design models assume exogenous customer demand that is independent of the service time or level. This paper examines the benefits of segmenting demand according to lead-time sensitivity of customers. To capture lead-time sensitivity in the network design model, we use a facility grouping method to ensure that the different demand classes are satisfied on time. In addition, we perform a series of computational experiments to develop a set of managerial insights for the network design decision making process.

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A Two-Level Framework For Coalition Formation Via Optimization And Agent Negotiation, Hoong Chuin Lau, Lei Zhang Sep 2004

A Two-Level Framework For Coalition Formation Via Optimization And Agent Negotiation, Hoong Chuin Lau, Lei Zhang

Research Collection School Of Computing and Information Systems

We present a two-level coalition formation approach based on a centralized optimization model on the upper level, and a distributed agent-negotiation model on the lower level. This approach allows us to balance agent self-interests against a high joint utility. Experimental results show that the two-level coalition formation mechanism will increase not only the overall utility of the coalition, but also the individual utility of most participating agents. The results also suggest it is better for the agents to be partially cooperative rather than either fully cooperative or self-interested in our setting.

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Towards Personalised Web Intelligence, Ah-Hwee Tan, Hwee-Leng Ong, Hong Pan, Jamie Ng, Qiu-Xiang Li Sep 2004

Towards Personalised Web Intelligence, Ah-Hwee Tan, Hwee-Leng Ong, Hong Pan, Jamie Ng, Qiu-Xiang Li

Research Collection School Of Computing and Information Systems

The Flexible Organizer for Competitive Intelligence (FOCI) is a personalised web intelligence system that provides an integrated platform for gathering, organising, tracking, and disseminating competitive information on the web. FOCI builds personalised information portfolios through a novel method called User-Configurable Clustering, which allows a user to personalise his/her portfolios in terms of the content as well as the organisational structure. This paper outlines the key challenges we face in personalised information management and gives a detailed account of FOCI’s underlying personalisation mechanism. For a quantitative evaluation of the system’s performance, we propose a set of performance indices based on information …

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