Effect Of Human Biases On Human-Agent Teams,
2010
Carnegie Mellon University
Effect Of Human Biases On Human-Agent Teams, Praveen Paruchuri, Pradeep Reddy Varakantham, Katia Sycara, Paul Scerri
Research Collection School Of Computing and Information Systems
As human-agent teams get increasingly deployed in the real-world, agent designers need to take into account that humans and agents have different abilities to specify preferences. In this paper, we focus on how human biases in specifying preferences for resources impacts the performance of large, heterogeneous teams. In particular, we model the inclination of humans to simplify their preference functions and to exaggerate their utility for desired resources, and show the effect of these biases on the team performance. We demonstrate this on two different problems, which are representative of many resource allocation problems addressed in literature. In both these …
On Decision Support For Deliberating With Constraints In Constrained Optimization Models,
2010
Singapore Management University
On Decision Support For Deliberating With Constraints In Constrained Optimization Models, Steven O. Kimbrough, Ann Kuo, Hoong Chuin Lau, David H. Wood
Research Collection School Of Computing and Information Systems
This paper introduces the Deliberation Decision Support System (DDSS). The DDSS obtains heuristically (using a genetic algorithm) solutions of interest for constrained optimization models. This is illustrated, without loss of generality, by generalized assignment problems. The DDSS also provides users with graphical tools that support post-solution deliberation for constrained optimization models. The DDSS and this paper, as befits practical concerns, are focused on deliberation with respect to the constraints of the models being used.
A First Practical Algorithm For High Levels Of Relational Consistency,
2010
University of Nebraska-Lincoln
A First Practical Algorithm For High Levels Of Relational Consistency, Shant Karakashian, Robert J. Woodward, Christopher Reesons, Berthe Y. Choueiry, Christian Bessiere
School of Computing: Conference and Workshop Papers
Consistency properties and algorithms for achieving them are at the heart of the success of Constraint Programming. In this paper, we study the relational consistency property R(∗,m)C, which is equivalent to m-wise consistency proposed in relational databases. We also define wR(∗,m)C, a weaker variant of this property. We propose an algorithm for enforcing these properties on a Constraint Satisfaction Problem by tightening the existing relations and without introducing new ones. We empirically show that wR(∗,m)C solves in a backtrack-free manner all the instances of some CSP benchmark classes, thus hinting at the tractability of those classes.
Mental Development And Representation Building Through Motivated Learning,
2010
Singapore Management University
Mental Development And Representation Building Through Motivated Learning, Janusz Starzyk, Pawel Raif, Ah-Hwee Tan
Research Collection School Of Computing and Information Systems
Motivated learning is a new machine learning approach that extends reinforcement learning idea to dynamically changing, and highly structured environments. In this approach a machine is capable of defining its own objectives and learns to satisfy them though an internal reward system. The machine is forced to explore the environment in response to externally applied negative (pain) signals that it must minimize. In doing so, it discovers relationships between objects observed through its sensory inputs and actions it performs on the observed objects. Observed concepts are not predefined but are emerging as a result of successful operations. For the optimum …
Proceedings Of The Sixth International Natural Language Generation Conference (Inlg 2010).,
2010
Technological University Dublin
Proceedings Of The Sixth International Natural Language Generation Conference (Inlg 2010)., John D. Kelleher, Brian Mac Namee, Ielka Van Der Sluis
Conference papers
No abstract provided.
Faceted Topic Retrieval Of News Video Using Joint Topic Modeling Of Visual Features And Speech Transcripts,
2010
Singapore Management University
Faceted Topic Retrieval Of News Video Using Joint Topic Modeling Of Visual Features And Speech Transcripts, Kong-Wah Wan, Ah-Hwee Tan, Joo-Hwee Lim, Liang-Tien Chia
Research Collection School Of Computing and Information Systems
Because of the inherent ambiguity in user queries, an important task of modern retrieval systems is faceted topic retrieval (FTR), which relates to the goal of returning diverse or novel information elucidating the wide range of topics or facets of the query need. We introduce a generative model for hypothesizing facets in the (news) video domain by combining the complementary information in the visual keyframes and the speech transcripts. We evaluate the efficacy of our multimodal model on the standard TRECVID-2005 video corpus annotated with facets. We find that: (1) the joint modeling of the visual and text (speech transcripts) …
Effective Heuristic Methods For Finding Non-Optimal Solutions Of Interest In Constrained Optimization Models,
2010
University of Pennsylvania
Effective Heuristic Methods For Finding Non-Optimal Solutions Of Interest In Constrained Optimization Models, Steven O. Kimbrough, Ann Kuo, Hoong Chuin Lau
Research Collection School Of Computing and Information Systems
This paper introduces the SoI problem, that of finding nonoptimal solutions of interest for constrained optimization models. SoI problems subsume finding FoIs (feasible solutions of interest), and IoIs (infeasible solutions of interest). In all cases, the interest addressed is post-solution analysis in one form or another. Post-solution analysis of a constrained optimization model occurs after the model has been solved and a good or optimal solution for it has been found. At this point, sensitivity analysis and other questions of import for decision making (discussed in the paper) come into play and for this purpose the SoIs can be of …
Anytime Planning For Decentralized Pomdps Using Expectation Maximization,
2010
Singapore Management University
Anytime Planning For Decentralized Pomdps Using Expectation Maximization, Akshat Kumar, Shlomo Zilberstein
Research Collection School Of Computing and Information Systems
Decentralized POMDPs provide an expressive framework for multi-agent sequential decision making. While finite-horizon DECPOMDPs have enjoyed signifcant success, progress remains slow for the infinite-horizon case mainly due to the inherent complexity of optimizing stochastic controllers representing agent policies. We present a promising new class of algorithms for the infinite-horizon case, which recasts the optimization problem as inference in a mixture of DBNs. An attractive feature of this approach is the straightforward adoption of existing inference techniques in DBNs for solving DEC-POMDPs and supporting richer representations such as factored or continuous states and actions. We also derive the Expectation Maximization (EM) …
Point-Based Backup For Decentralized Pompds: Complexity And New Algorithms,
2010
Singapore Management University
Point-Based Backup For Decentralized Pompds: Complexity And New Algorithms, Akshat Kumar, Shlomo Zilberstein
Research Collection School Of Computing and Information Systems
Decentralized POMDPs provide an expressive framework for sequential multi-agent decision making. Despite their high complexity, there has been significant progress in scaling up existing algorithms, largely due to the use of point-based methods. Performing point-based backup is a fundamental operation in state-of-the-art algorithms. We show that even a single backup step in the multi-agent setting is NP-Complete. Despite this negative worst-case result, we present an efficient and scalable optimal algorithm as well as a principled approximation scheme. The optimal algorithm exploits recent advances in the weighted CSP literature to overcome the complexity of the backup operation. The polytime approximation scheme …
Handling Concept Drift In Text Data Stream Constrained By High Labelling Cost,
2010
Technological University Dublin
Handling Concept Drift In Text Data Stream Constrained By High Labelling Cost, Patrick Lindstrom, Sarah Jane Delany, Brian Mac Namee
Conference papers
In many real-world classification problems the concept being modelled is not static but rather changes over time - a situation known as concept drift. Most techniques for handling concept drift rely on the true classifications of test instances being available shortly after classification so that classifiers can be retrained to handle the drift. However, in applications where labelling instances with their true class has a high cost this is not reasonable. In this paper we present an approach for keeping a classifier up-to-date in a concept drift domain which is constrained by a high cost of labelling. We use …
A Study Of Three Artificial Neural Networks Models' Ability To Identify Emotions From Facial Images,
2010
Columbus State University
A Study Of Three Artificial Neural Networks Models' Ability To Identify Emotions From Facial Images, Timothy Scott Hyde
Theses and Dissertations
Facial expressions conveying emotions are vital for human communication. They are also important in the studies of human interaction and behavioral studies. Recognition of emotions, using facial images, may provide a fast and practical approach that is noninvasive. Most previous studies of emotion recognition through facial images were based on the Facial Action Coding System (FACS). The FACS, which was developed by Ekman and Freisen in 1978, was created to identify different facial muscular actions. Previous artificial neural network-based approaches for classification of facial expressions focused on improving one particular neural network model for better accuracy. The purpose of this …
Architecture Optimization, Training Convergence And Network Estimation Robustness Of A Fully Connected Recurrent Neural Network,
2010
Clemson University
Architecture Optimization, Training Convergence And Network Estimation Robustness Of A Fully Connected Recurrent Neural Network, Xiaoyu Wang
All Dissertations
Recurrent neural networks (RNN) have been rapidly developed in recent years. Applications of RNN can be found in system identification, optimization, image processing, pattern reorganization, classification, clustering, memory association, etc.
In this study, an optimized RNN is proposed to model nonlinear dynamical systems. A fully connected RNN is developed first which is modified from a fully forward connected neural network (FFCNN) by accommodating recurrent connections among its hidden neurons. In addition, a destructive structure optimization algorithm is applied and the extended Kalman filter (EKF) is adopted as a network's training algorithm. These two algorithms can seamlessly work together to generate …
Towards Finding Robust Execution Strategies For Rcpsp/Max With Durational Uncertainty,
2010
Singapore Management University
Towards Finding Robust Execution Strategies For Rcpsp/Max With Durational Uncertainty, Na Fu, Pradeep Varakantham, Hoong Chuin Lau
Research Collection School Of Computing and Information Systems
Resource Constrained Project Scheduling Problems with minimum and maximum time lags (RCPSP/max) have been studied extensively in the literature. However, the more realistic RCPSP/max problems — ones where durations of activities are not known with certainty – have received scant interest and hence are the main focus of the paper. Towards addressing the significant computational complexity involved in tackling RCPSP/max with durational uncertainty, we employ a local search mechanism to generate robust schedules. In this regard, we make two key contributions: (a) Introducing and studying the key properties of a new decision rule to specify start times of activities with …
Optimization And Analysis Of A Robotic Navigational Algorithm,
2010
Illinois Wesleyan University
Optimization And Analysis Of A Robotic Navigational Algorithm, Derek Carlson, Joshua Brown Kramer, Faculty Advisor
John Wesley Powell Student Research Conference
The problem of robot navigation involves planning a path to move a robot from a start point to a known target point within an obstacle course. The efficiency of such an algorithm can be measured in several ways. For instance, Lumelsky and Stepanov measure the length of the path taken in terms of obstacle perimeters. Gabriely and Rimon compare their two-dimensional algorithm's efficiency to that of the optimal algorithm. Brown Kramer and Sabalka expand upon the work of Gabriely and Rimon to produce an algorithm for dimensions greater than two. The primary objective of this research was to implement improvements …
Artificial Intelligence: Soon To Be The World’S Greatest Intelligence, Or Just A Wild Dream?,
2010
Johnson & Wales University - Providence
Artificial Intelligence: Soon To Be The World’S Greatest Intelligence, Or Just A Wild Dream?, Edward R. Kollett
Academic Symposium of Undergraduate Scholarship
The purpose of the paper was to examine the field of artificial intelligence. In particular, the paper focused on what has been accomplished towards the goal of making a machine that can think like a human, and the hardships that researchers in the field has faced. It also touched upon the potential outcomes of success. Why is this paper important? As computers become more powerful, the common conception is that they are becoming more intelligent. As computers become more integrated with society and more connected with each other, people again believe they are becoming smarter. Therefore, it is important that …
Coalition Formation Under Uncertainty,
2010
Air Force Institute of Technology
Coalition Formation Under Uncertainty, Daylond J. Hooper
Theses and Dissertations
Many multiagent systems require allocation of agents to tasks in order to ensure successful task execution. Most systems that perform this allocation assume that the quantity of agents needed for a task is known beforehand. Coalition formation approaches relax this assumption, allowing multiple agents to be dynamically assigned. Unfortunately, many current approaches to coalition formation lack provisions for uncertainty. This prevents application of coalition formation techniques to complex domains, such as real-world robotic systems and agent domains where full state knowledge is not available. Those that do handle uncertainty have no ability to handle dynamic addition or removal of agents …
Developing An Effective And Efficient Real Time Strategy Agent For Use As A Computer Generated Force,
2010
Air Force Institute of Technology
Developing An Effective And Efficient Real Time Strategy Agent For Use As A Computer Generated Force, Kurt Weissgerber
Theses and Dissertations
Computer Generated Forces (CGF) are used to represent units or individuals in military training and constructive simulation. The use of CGF significantly reduces the time and money required for effective training. For CGF to be effective, they must behave as a human would in the same environment. Real Time Strategy (RTS) games place players in control of a large force whose goal is to defeat the opponent. The military setting of RTS games makes them an excellent platform for the development and testing of CGF. While there has been significant research in RTS agent development, most of the developed agents …
Evolutionary Artificial Neural Network Weight Tuning To Optimize Decision Making For An Abstract Game,
2010
Air Force Institute of Technology
Evolutionary Artificial Neural Network Weight Tuning To Optimize Decision Making For An Abstract Game, Corey M. Miller
Theses and Dissertations
Abstract strategy games present a deterministic perfect information environment with which to test the strategic capabilities of artificial intelligence systems. With no unknowns or random elements, only the competitors’ performances impact the results. This thesis takes one such game, Lines of Action, and attempts to develop a competitive heuristic. Due to the complexity of Lines of Action, artificial neural networks are utilized to model the relative values of board states. An application, pLoGANN (Parallel Lines of Action with Genetic Algorithm and Neural Networks), is developed to train the weights of this neural network by implementing a genetic algorithm over a …
Autonomous Satellite Operations For Cubesat Satellites,
2010
California Polytechnic State University, San Luis Obispo
Autonomous Satellite Operations For Cubesat Satellites, Jason Lionel Anderson
Master's Theses
In the world of educational satellites, student teams manually conduct operations daily, sending commands and collecting downlinked data. Educational satellites typically travel in a Low Earth Orbit allowing line of sight communication for approximately thirty minutes each day. This is manageable for student teams as the required manpower is minimal. The international Global Educational Network for Satellite Operations (GENSO), however, promises satellite contact upwards of sixteen hours per day by connecting earth stations all over the world through the Internet. This dramatic increase in satellite communication time is unreasonable for student teams to conduct manual operations and alternatives must be …
Designing Successful Online Courses - Part 2,
2010
University of South Florida
Designing Successful Online Courses - Part 2, Kathleen P. King
Department of Leadership, Policy, and Lifelong Learning
Once again, our major goal is to provide faculty with consistent guidance through the many instructional decisions and design steps they need to pursue in this process. This process is a fantastic opportunity to craft a virtual learning space in which people can engaging in learning beyond the constraints of time and space.
