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Research Collection School Of Computing and Information Systems

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Articles 1531 - 1560 of 1664

Full-Text Articles in Artificial Intelligence and Robotics

Fine-Tuning Algorithm Parameters Using The Design Of Experiments Approach, Aldy Gunawan, Hoong Chuin Lau, Linda Lindawati Jan 2011

Fine-Tuning Algorithm Parameters Using The Design Of Experiments Approach, Aldy Gunawan, Hoong Chuin Lau, Linda Lindawati

Research Collection School Of Computing and Information Systems

Optimizing parameter settings is an important task in algorithm design. Several automated parameter tuning procedures/configurators have been proposed in the literature, most of which work effectively when given a good initial range for the parameter values. In the Design of Experiments (DOE), a good initial range is known to lead to an optimum parameter setting. In this paper, we present a framework based on DOE to find a good initial range of parameter values for automated tuning. We use a factorial experiment design to first screen and rank all the parameters thereby allowing us to then focus on the parameter …

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Would Price Limits Have Made Any Difference To The 'Flash Crash' On May 6, 2010, Wing Bernard Lee, Shih-Fen Cheng, Annie Koh Jan 2011

Would Price Limits Have Made Any Difference To The 'Flash Crash' On May 6, 2010, Wing Bernard Lee, Shih-Fen Cheng, Annie Koh

Research Collection School Of Computing and Information Systems

On May 6, 2010, the U.S. equity markets experienced a brief but highly unusual drop in prices across a number of stocks and indices. The Dow Jones Industrial Average (see Figure 1) fell by approximately 9% in a matter of minutes, and several stocks were traded down sharply before recovering a short time later. The authors contend that the events of May 6, 2010 exhibit patterns consistent with the type of "flash crash" observed in their earlier study (2010). This paper describes the results of nine different simulations created by using a large-scale computer model to reconstruct the critical elements …

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Map Estimation For Graphical Models By Likelihood Maximization, Akshat Kumar, Shlomo Zilberstein Dec 2010

Map Estimation For Graphical Models By Likelihood Maximization, Akshat Kumar, Shlomo Zilberstein

Research Collection School Of Computing and Information Systems

Computing a maximum a posteriori (MAP) assignment in graphical models is a crucial inference problem for many practical applications. Several provably convergent approaches have been successfully developed using linear programming (LP) relaxation of the MAP problem. We present an alternative approach, which transforms the MAP problem into that of inference in a finite mixture of simple Bayes nets. We then derive the Expectation Maximization (EM) algorithm for this mixture that also monotonically increases a lower bound on the MAP assignment until convergence. The update equations for the EM algorithm are remarkably simple, both conceptually and computationally, and can be implemented …

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Semi-Autonomous Virtual Valet Parking, Arne Suppe, Luis Navarro-Serment, Aaron Steinfeld Nov 2010

Semi-Autonomous Virtual Valet Parking, Arne Suppe, Luis Navarro-Serment, Aaron Steinfeld

Research Collection School Of Computing and Information Systems

Despite regulations specifying parking spots that support wheelchair vans, it is not uncommon for end users to encounter problems with clearance for van ramps. Even if a driver elects to park in the far reaches of a parking lot as a precautionary measure, there is no guarantee that the spot next to their van will be empty when they return. Likewise, the prevalence of older drivers who experience significant difficulty with ingress and egress from vehicles is nontrivial and the ability to fully open a car door is important. This work describes a method and user interaction for low cost, …

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Event Study Method For Validating Agent-Based Trading Simulations, Shih-Fen Cheng Sep 2010

Event Study Method For Validating Agent-Based Trading Simulations, Shih-Fen Cheng

Research Collection School Of Computing and Information Systems

In this paper, we introduce how one can validate an event-centric trading simulation platform that is built with multi-agent technology. The issue of validation is extremely important for agent-based simulations, but unfortunately, so far there is no one universal method that would work in all domains. The primary contribution of this paper is a novel combination of event-centric simulation design and event study approach for market dynamics generation and validation. In our event-centric design, the simulation is progressed by announcing news events that affect market prices. Upon receiving these events, event-aware software agents would adjust their views on the market …

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A Biologically-Inspired Cognitive Agent Model Integrating Declarative Knowledge And Reinforcement Learning, Ah-Hwee Tan, Gee-Wah Ng Sep 2010

A Biologically-Inspired Cognitive Agent Model Integrating Declarative Knowledge And Reinforcement Learning, Ah-Hwee Tan, Gee-Wah Ng

Research Collection School Of Computing and Information Systems

The paper proposes a biologically-inspired cognitive agent model, known as FALCON-X, based on an integration of the Adaptive Control of Thought (ACT-R) architecture and a class of self-organizing neural networks called fusion Adaptive Resonance Theory (fusion ART). By replacing the production system of ACT-R by a fusion ART model, FALCON-X integrates high-level deliberative cognitive behaviors and real-time learning abilities, based on biologically plausible neural pathways. We illustrate how FALCON-X, consisting of a core inference area interacting with the associated intentional, declarative, perceptual, motor and critic memory modules, can be used to build virtual robots for battles in a simulated RoboCode …

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On Decision Support For Deliberating With Constraints In Constrained Optimization Models, Steven O. Kimbrough, Ann Kuo, Hoong Chuin Lau, David H. Wood Aug 2010

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.

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The Bi-Objective Master Physician Scheduling Problem, Aldy Gunawan, Hoong Chuin Lau Aug 2010

The Bi-Objective Master Physician Scheduling Problem, Aldy Gunawan, Hoong Chuin Lau

Research Collection School Of Computing and Information Systems

Physician scheduling is the assignment of physicians to perform different duties in the hospital timetable. In this paper, the goals are to satisfy as many physicians’ preferences and duty requirements as possible while ensuring optimum usage of available resources. We present a mathematical programming model to represent the problem as a bi-objective optimization problem. Three different methods based on ε–Constraint Method, Weighted-Sum Method and HillClimbing algorithm are proposed. These methods were tested on a real case from the Surgery Department of a large local government hospital, as well as on randomly generated problem instances. The strengths and weaknesses of the …

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Decentralized Resource Allocation And Scheduling Via Walrasian Auctions With Negotiable Agents, Huaxing Chen, Hoong Chuin Lau Aug 2010

Decentralized Resource Allocation And Scheduling Via Walrasian Auctions With Negotiable Agents, Huaxing Chen, Hoong Chuin Lau

Research Collection School Of Computing and Information Systems

This paper is concerned with solving decentralized resource allocation and scheduling problems via auctions with negotiable agents by allowing agents to switch their bid generation strategies within the auction process, such that a better system wide performance is achieved on average as compared to the conventional walrasian auction running with agents of fixed bid generation strategy. We propose a negotiation mechanism embedded in auctioneer to solicit bidders’ change of strategies in the process of auction. Finally we benchmark our approach against conventional auctions subject to the real-time large-scale dynamic resource coordination problem to demonstrate the effectiveness of our approach.

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Distributed Route Planning And Scheduling Via Hybrid Conflict Resolution, Ramesh Thangarajoo, Hoong Chuin Lau Aug 2010

Distributed Route Planning And Scheduling Via Hybrid Conflict Resolution, Ramesh Thangarajoo, Hoong Chuin Lau

Research Collection School Of Computing and Information Systems

In this paper, we discuss the problem of route planning and scheduling by a group of agents. Each agent is responsible for designing a route plan and schedule over a geographical network, and the goal is to obtain a conflict-free plan/schedule that optimizes a global objective. We present a hybrid conflict resolution method that involves coalition formation and distributed constraint satisfaction depending on the level of coupling between agents. We show how this approach can be effectively applied to solve a distributed convoy movement planning problem.

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A Decision Theoretic Approach To Data Leakage Prevention, Janusz Marecki, Mudhakar Srivastava, Pradeep Reddy Varakantham Aug 2010

A Decision Theoretic Approach To Data Leakage Prevention, Janusz Marecki, Mudhakar Srivastava, Pradeep Reddy Varakantham

Research Collection School Of Computing and Information Systems

In both the commercial and defense sectors a compelling need is emerging for rapid, yet secure, dissemination of information. In this paper we address the threat of information leakage that often accompanies such information flows. We focus on domains with one information source (sender) and many information sinks (recipients) where: (i) sharing is mutually beneficial for the sender and the recipients, (ii) leaking a shared information is beneficial to the recipients but undesirable to the sender, and (iii) information sharing decisions of the sender are determined using imperfect monitoring of the (un)intended information leakage by the recipients.We make two key …

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Effect Of Human Biases On Human-Agent Teams, Praveen Paruchuri, Pradeep Reddy Varakantham, Katia Sycara, Paul Scerri Aug 2010

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 …

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Effective Heuristic Methods For Finding Non-Optimal Solutions Of Interest In Constrained Optimization Models, Steven O. Kimbrough, Ann Kuo, Hoong Chuin Lau Jul 2010

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 …

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Mental Development And Representation Building Through Motivated Learning, Janusz Starzyk, Pawel Raif, Ah-Hwee Tan Jul 2010

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 …

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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 Jul 2010

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) …

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Anytime Planning For Decentralized Pomdps Using Expectation Maximization, Akshat Kumar, Shlomo Zilberstein Jun 2010

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) …

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Point-Based Backup For Decentralized Pompds: Complexity And New Algorithms, Akshat Kumar, Shlomo Zilberstein May 2010

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 …

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Towards Finding Robust Execution Strategies For Rcpsp/Max With Durational Uncertainty, Na Fu, Pradeep Varakantham, Hoong Chuin Lau May 2010

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 …

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An Analysis Of Extreme Price Shocks And Illiquidity Among Trend Followers, Bernard Lee, Shih-Fen Cheng, Annie Koh Feb 2010

An Analysis Of Extreme Price Shocks And Illiquidity Among Trend Followers, Bernard Lee, Shih-Fen Cheng, Annie Koh

Research Collection School Of Computing and Information Systems

We construct an agent-based model to study the interplay between extreme price shocks and illiquidity in the presence of systematic traders known as trend followers. The agent-based approach is particularly attractive in modeling commodity markets because the approach allows for the explicit modeling of production, capacities, and storage constraints. Our study begins by using the price stream from a market simulation involving human participants and studies the behavior of various trend-following strategies, assuming initially that their participation will not impact the market. We notice an incremental deterioration in strategy performance as and when strategies deviate further and further from the …

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A Boosting Framework For Visuality-Preserving Distance Metric Learning And Its Application To Medical Image Retrieval, Yang Liu, Rong Jin, Lily Mummert, Rahul Sukthankar, Adam Goode, Bin Zheng, Steven C. H. Hoi, Mahadev Satyanarayanan Jan 2010

A Boosting Framework For Visuality-Preserving Distance Metric Learning And Its Application To Medical Image Retrieval, Yang Liu, Rong Jin, Lily Mummert, Rahul Sukthankar, Adam Goode, Bin Zheng, Steven C. H. Hoi, Mahadev Satyanarayanan

Research Collection School Of Computing and Information Systems

Similarity measurement is a critical component in content-based image retrieval systems, and learning a good distance metric can significantly improve retrieval performance. However, despite extensive study, there are several major shortcomings with the existing approaches for distance metric learning that can significantly affect their application to medical image retrieval. In particular, "similarity" can mean very different things in image retrieval: resemblance in visual appearance (e.g., two images that look like one another) or similarity in semantic annotation (e.g., two images of tumors that look quite different yet are both malignant). Current approaches for distance metric learning typically address only one …

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Periodic Resource Reallocation In Two-Echelon Repairable Item Inventory Systems, Hoong Chuin Lau, Jie Pan, Huawei Song Jan 2010

Periodic Resource Reallocation In Two-Echelon Repairable Item Inventory Systems, Hoong Chuin Lau, Jie Pan, Huawei Song

Research Collection School Of Computing and Information Systems

Given an existing stock allocation in an inventory system, it is often necessary to perform reallocation over multiple time points to address inventory imbalance and maximize availability. In this paper, we focus on the situation where there are two opportunities to perform reallocation within a replenishment cycle. We derive a mathematical model to determine when and how to perform reallocation. Furthermore, we consider the extension of this model to the situation allowing an arbitrary number of reallocations. Experimental results show that the two-reallocation approach achieves better performance compared with the single-reallocation approach found in the literature. We also illustrate how …

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Introducing Communication In Dis-Pomdps With Locality Of Interaction, Makoto Tasaki, Yuichi Yabu, Yuki Iwanari, Makoto Yokoo, Janusz Marecki, Pradeep Reddy Varakantham, Milind Tambe Jan 2010

Introducing Communication In Dis-Pomdps With Locality Of Interaction, Makoto Tasaki, Yuichi Yabu, Yuki Iwanari, Makoto Yokoo, Janusz Marecki, Pradeep Reddy Varakantham, Milind Tambe

Research Collection School Of Computing and Information Systems

The Networked Distributed POMDPs (ND-POMDPs) can model multiagent systems in uncertain domains and has begun to scale-up the number of agents. However, prior work in ND-POMDPs has failed to address communication. Without communication, the size of a local policy at each agent within the ND-POMDPs grows exponentially in the time horizon. To overcome this problem, we extend existing algorithms so that agents periodically communicate their observation and action histories with each other. After communication, agents can start from new synchronized belief state. Thus, we can avoid the exponential growth in the size of local policies at agents. Furthermore, we introduce …

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Motivated Learning As An Extension Of Reinforcement Learning, Janusz Starzyk, Pawel Raif, Ah-Hwee Tan Jan 2010

Motivated Learning As An Extension Of Reinforcement Learning, Janusz Starzyk, Pawel Raif, Ah-Hwee Tan

Research Collection School Of Computing and Information Systems

We have developed a unified framework to conduct computational experiments with both learning systems: Motivated learning based on Goal Creation System, and reinforcedment learning using RL Q-Learning Algorithm. Future work includes combining motivated learning to set abstract motivations and manage goals with reinforcement learning to learn proper actions. This will allow testing of motivated learning on typical reinforcement learning benchmarks with large dimensionality of the state/action spaces.

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A Service Choice Model For Optimizing Taxi Service Delivery, Shih-Fen Cheng, Xin Qu Oct 2009

A Service Choice Model For Optimizing Taxi Service Delivery, Shih-Fen Cheng, Xin Qu

Research Collection School Of Computing and Information Systems

Taxi service has undergone radical revamp in recent years. In particular, significant investments in communication system and GPS devices have improved quality of taxi services through better dispatches. In this paper, we propose to leverage on such infrastructure and build a service choice model that helps individual drivers in deciding whether to serve a specific taxi stand or not. We demonstrate the value of our model by applying it to a real-world scenario. We also highlight interesting new potential approaches that could significantly improve the quality of taxi services.

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Sharing Mobile Multimedia Annotations To Support Inquiry-Based Learning Using Mobitop, Khasfariyati Razikin, Dion Hoe-Lian Goh, Yin-Leng Theng, Quang Minh Nguyen, Thi Nhu Quynh Kim, Ee Peng Lim, Chew-Hung Chang, Kalyani Chatterjea, Aixin Sun Oct 2009

Sharing Mobile Multimedia Annotations To Support Inquiry-Based Learning Using Mobitop, Khasfariyati Razikin, Dion Hoe-Lian Goh, Yin-Leng Theng, Quang Minh Nguyen, Thi Nhu Quynh Kim, Ee Peng Lim, Chew-Hung Chang, Kalyani Chatterjea, Aixin Sun

Research Collection School Of Computing and Information Systems

Mobile devices used in educational settings are usually employed within a collaborative learning activity in which learning takes place in the form of social interactions between team members while performing a shared task. We introduce MobiTOP (Mobile Tagging of Objects and People), a geospatial digital library system which allows users to contribute and share multimedia annotations via mobile devices. A key feature of MobiTOP that is well suited for collaborative learning is that annotations are hierarchical, allowing annotations to be annotated by other users to an arbitrary depth. A group of student-teachers involved in an inquiry-based learning activity in geography …

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A Surprise Triggered Adaptive And Reactive (Star) Framework For Online Adaptation In Non-Stationary Environments, Truong-Huy Dinh Nguyen, Tze-Yun Leong Oct 2009

A Surprise Triggered Adaptive And Reactive (Star) Framework For Online Adaptation In Non-Stationary Environments, Truong-Huy Dinh Nguyen, Tze-Yun Leong

Research Collection School Of Computing and Information Systems

We consider the task of developing an adaptive autonomous agent that can interact with non-stationary environments. Traditional learning approaches such as Reinforcement Learning assume stationary characteristics over the course of the problem, and are therefore unable to learn the dynamically changing settings correctly. We introduce a novel adaptive framework that can detect dynamic changes due to non-stationary elements. The Surprise Triggered Adaptive and Reactive (STAR) framework is inspired by human adaptability in dealing with daily life changes. An agent adopting the STAR framework consists primarily of two components, Adapter and Reactor. The Reactor chooses suitable actions based on predictions made …

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Localizing Volumetric Motion For Action Recognition In Realistic Videos, Xiao Wu, Chong-Wah Ngo, Jintao Li, Yongdong Zhang Oct 2009

Localizing Volumetric Motion For Action Recognition In Realistic Videos, Xiao Wu, Chong-Wah Ngo, Jintao Li, Yongdong Zhang

Research Collection School Of Computing and Information Systems

This paper presents a novel motion localization approach for recognizing actions and events in real videos. Examples include StandUp and Kiss in Hollywood movies. The challenge can be attributed to the large visual and motion variations imposed by realistic action poses. Previous works mainly focus on learning from descriptors of cuboids around space time interest points (STIP) to characterize actions. The size, shape and space-time position of cuboids are fixed without considering the underlying motion dynamics. This often results in large set of fragmentized cuboids which fail to capture long-term dynamic properties of realistic actions. This paper proposes the detection …

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Font Size: Make Font Size Smaller Make Font Size Default Make Font Size Larger Exploiting Coordination Locales In Distributed Pomdps Via Social Model Shaping, Pradeep Varakantham, Jun Young Kwak, Matthew Taylor, Janusz Marecki, Paul Scerri, Milind Tambe Sep 2009

Font Size: Make Font Size Smaller Make Font Size Default Make Font Size Larger Exploiting Coordination Locales In Distributed Pomdps Via Social Model Shaping, Pradeep Varakantham, Jun Young Kwak, Matthew Taylor, Janusz Marecki, Paul Scerri, Milind Tambe

Research Collection School Of Computing and Information Systems

Distributed POMDPs provide an expressive framework for modeling multiagent collaboration problems, but NEXPComplete complexity hinders their scalability and application in real-world domains. This paper introduces a subclass of distributed POMDPs, and TREMOR, an algorithm to solve such distributed POMDPs. The primary novelty of TREMOR is that agents plan individually with a single agent POMDP solver and use social model shaping to implicitly coordinate with other agents. Experiments demonstrate that TREMOR can provide solutions orders of magnitude faster than existing algorithms while achieving comparable, or even superior, solution quality.

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Multi-View Ear Recognition Based On Moving Least Square Pose Interpolation, Heng Liu, David Zhang, Zhiyuan Zhang Sep 2009

Multi-View Ear Recognition Based On Moving Least Square Pose Interpolation, Heng Liu, David Zhang, Zhiyuan Zhang

Research Collection School Of Computing and Information Systems

Based on moving least square, a multi-view ear pose interpolation and corresponding recognition approach is proposed. This work firstly analyzes the shape characteristics of actual trace caused by ear pose varying in feature space. Then according to training samples pose projection, we manage to recover the complete multi-view ear pose manifold by using moving least square pose interpolation. The constructed multi-view ear pose manifolds can be easily utilized to recognize ear images captured under different views based on finding the minimal projection distance to the manifolds. The experimental results and some comparisons show the new method is superior to manifold …

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Setting Discrete Bid Levels Adaptively In Repeated Auctions, Jilian Zhang, Hoong Chuin Lau, Jialie Shen Aug 2009

Setting Discrete Bid Levels Adaptively In Repeated Auctions, Jilian Zhang, Hoong Chuin Lau, Jialie Shen

Research Collection School Of Computing and Information Systems

The success of an auction design often hinges on its ability to set parameters such as reserve price and bid levels that will maximize an objective function such as the auctioneer revenue. Works on designing adaptive auction mechanisms have emerged recently, and the challenge is in learning different auction parameters by observing the bidding in previous auctions. In this paper, we propose a non-parametric method for determining discrete bid levels dynamically so as to maximize the auctioneer revenue. First, we propose a non-parametric kernel method for estimating the probabilities of closing price with past auction data. Then a greedy strategy …

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