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Articles 1 - 30 of 36
Full-Text Articles in Computer Sciences
Integration Of Probabilistic Graphic Models For Decision Support, Jiang C., Poh K., Tze-Yun Leong
Integration Of Probabilistic Graphic Models For Decision Support, Jiang C., Poh K., Tze-Yun Leong
Research Collection School Of Computing and Information Systems
It is a frequently encountered problem that new knowledge arrived when making decisions in a dynamic world. Usually, domain experts cannot afford enough time and knowledge to effectively assess and combine both qualitative and quantitative information in these models. Existing approaches can solve only one of two tasks instead of both. We propose a four-step algorithm to integrate multiple probabilistic graphic models, which can effectively update existing models with newly acquired models. In this algorithm, the qualitative part of model integration is performed first, followed by the quantitative combination. We illustrate our method with an example of combining three models. …
A Generic Object-Oriented Tabu Search Framework, Hoong Chuin Lau, Xiaomin Jia, Wee Chong Wan
A Generic Object-Oriented Tabu Search Framework, Hoong Chuin Lau, Xiaomin Jia, Wee Chong Wan
Research Collection School Of Computing and Information Systems
Presently, most tabu search designers devise their applications without considering the potential of design and code reuse, which consequently prolong the development of subsequent applications. In this paper, we propose a software solution known as Tabu Search Framework (TSF), which is a generic C++ software framework for tabu search implementation. The framework excels in code recycling through the use of a well- designed set of generic abstract classes that clearly define their collaborative roles in the algorithm. Additionally, the framework incorporates a centralized process and control mechanism that enhances the search with intelligence. This results in a generic framework that …
Enhancing Undergraduate Ai Courses Through Machine Learning Projects, Ingrid Russell, Zdravko Markov, Todd W. Neller, Susan Coleman
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 …
Solving Generalized Open Constraint Optimization Problem Using Two-Level Multi-Agent Framework, Hoong Chuin Lau, Lei Zhang, Chang Liu
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 …
Cooperative Reinforcement Learning Using An Expert-Measuring Weighted Strategy With Wolf, Kevin Cousin, Gilbert L. Peterson
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 …
Service-Oriented E-Learning Architecture Using Web Service-Based Intelligent Agents, Nasir Hussain, M. Khalid Khan
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 …
Poster Session A: Fingerprint Matching Using Ridge Patterns, Muhammad Umer Munir, Dr. Muhammad Younus Javed
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 …
Poster Session A: Face Recognition Using Sub-Holistic Pca, Muhammad Murtaza Khan, Dr. Muhammad Younus Javed, Muhammad Almas Anjum
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.
Tuning Tabu Search Strategies Via Visual Diagnosis, Steven Halim, Wee Chong Wan, Hoong Chuin Lau
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 …
Evaluation Of Time-Varying Availability In Multi-Echelon Inventory System With Combat Damage, Hoong Chuin Lau, Huawei Song
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.
Exploiting Belief Bounds: Practical Pomdps For Personal Assistant Agents, Pradeep Varakantham, Rajiv Maheswaran, Milind Tambe
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 …
Approximate Strategic Reasoning Through Hierarchical Reduction Of Large Symmetric Games, Michael P. Wellman, Daniel M. Reeves, Kevin M. Lochner, Shih-Fen Cheng, Rahul Suri
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.
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
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 …
Evaluating Online Trust Using Machine Learning Methods, Weihua Song
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 …
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
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 …
The Ames Mer Microscopic Imager Toolkit, Randy Sargent, Matt Deans, Clay Kunz, Ken Herkenhoff
The Ames Mer Microscopic Imager Toolkit, Randy Sargent, Matt Deans, Clay Kunz, Ken Herkenhoff
Randy Sargent
The Mars Exploration Rovers, spirit and opportunity, have spent several successful months on Mars, returning gigabytes of images and spectral data to scientists on Earth. One of the instruments on the MER rovers, the Athena microscopic imager (MI), is a fixed focus, megapixel camera providing a ±3mm depth of field and a 31×31 mm field of view at a working distance of 63 mm from the lens to the object being imaged. In order to maximize the science return from this instrument, we developed the Ames MI toolkit and supported its use during the primary mission. The MI toolkit is …
Unifying An Introduction To Artificial Intelligence Course Through Machine Learning Laboratory Experiences, Ingrid Russell, Zdravko Markov, Todd W. Neller, Michael Georgiopoulos, Susan Coleman
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 …
Fibonacci In Contextures, An Application, Rudolf Kaehr
Fibonacci In Contextures, An Application, Rudolf Kaehr
Rudolf Kaehr
No abstract provided.
Contextures. Programming Dynamic Complexity, Rudolf Kaehr
Contextures. Programming Dynamic Complexity, Rudolf Kaehr
Rudolf Kaehr
No abstract provided.
Gödel Games: "Cloning Gödel's Proofs", Rudolf Kaehr
Gödel Games: "Cloning Gödel's Proofs", Rudolf Kaehr
Rudolf Kaehr
Gödel's Proofs in the context of beautifying (Hehner) and re-beautifying in polycontextural logic. Deconstruction of the relevance.
Lambda Calculi In Polycontextural Situations, Rudolf Kaehr
Lambda Calculi In Polycontextural Situations, Rudolf Kaehr
Rudolf Kaehr
No abstract provided.
Polylogics. Towards A Formalization Of Polycontextural Logics, Rudolf Kaehr
Polylogics. Towards A Formalization Of Polycontextural Logics, Rudolf Kaehr
Rudolf Kaehr
No abstract provided.
Review Of Sweet Dreams: Philosophical Obstacles To A Science Of Consciousness, Leslie Marsh
Review Of Sweet Dreams: Philosophical Obstacles To A Science Of Consciousness, Leslie Marsh
Leslie Marsh
The question of how a physical system gives rise to the phenomenal or experiential (olfactory, visual, somatosensitive, gestatory and auditory), is considered the most intractable of scientific and philosophical puzzles. Though this question has dominated the philosophy of mind over the last quarter century, it articulates a version of the age-old mind–body problem. The most famous response, Cartesian dualism, is on Daniel Dennett’s view still a corrosively residual and redundant feature of popular (and academic) thinking on these matters. Fifteen years on from his anti-Cartesian theory of consciousness (Consciousness Explained, 1991), Dennett’s frustration with this tradition is still palpable. This …
A Context-Dependent Model Of Proximity In Physically Situated Environments, John D. Kelleher, Geert-Jan M. Kruijff
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 …
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, …
Critical Questions In Computational Models Of Legal Argument, Douglas Walton, Thomas F. Gordon
Critical Questions In Computational Models Of Legal Argument, Douglas Walton, Thomas F. Gordon
CRRAR Publications
Two recent computational models of legal argumentation, by Verheij and Gordon respectively, have interpreted critical questions as premises of arguments that can be defeated using Pollock’s concepts of undercutters and rebuttals. Using the scheme for arguments from expert opinion as an example, this paper evaluates and compares these two models of critical questions from the perspective of argumentation theory and competing legal theories about proof standardsfor defeating presumptions. The applicable proof standard is found to be a legal issue subject to argument. Verheij’smodel is shown to have problems because the proof stan-dards it applies to different kinds of premises are …
Sonar Sensor Interpretation For Ectogeneous Robots, Wen Gao
Sonar Sensor Interpretation For Ectogeneous Robots, Wen Gao
Dissertations, Theses, and Masters Projects
We have developed four generations of sonar scanning systems to automatically interpret surrounding environment. The first two are stationary 3D air-coupled ultrasound scanning systems and the last two are packaged as sensor heads for mobile robots. Template matching analysis is applied to distinguish simple indoor objects. It is conducted by comparing the tested echo with the reference echoes. Important features are then extracted and drawn in the phase plane. The computer then analyzes them and gives the best choices of the tested echoes automatically. For cylindrical objects outside, an algorithm has been presented to distinguish trees from smooth circular poles …
Terminator Or Super Mario: Human/Computer Hybrids, Actual And Virtual, Noreen L. Herzfeld
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 …
A Multi-Agent Approach For Solving Optimization Problems Involving Expensive Resources, Hoong Chuin Lau, H. Wang
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.
An Assessment Of Case-Based Reasoning For Spam Filtering, Sarah Jane Delany, Padraig Cunningham, Lorcan Coyle
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 …