Operations Research, Systems Engineering and Industrial Engineering Commons^™

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 …

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.

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.

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 …

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 …

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 …

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 …

Hybrid Time-Frequency Domain Analysis For Inverter-Fed Induction Motor Fault Detection, T. W. Chua, W. W. Tan, Zhaoxia Wang, C. S. Chang

Research Collection School Of Computing and Information Systems

The detection of faults in an induction motor is important as a part of preventive maintenance. Stator current is one of the most popular signals used for utility-supplied induction motor fault detection as a current sensor can be installed nonintrusively. In variable speeds operation, the use of an inverter to drive the induction motor introduces noise into the stator current so stator current based fault detection techniques become less reliable. This paper presents a hybrid algorithm, which combines time and frequency domain analysis, for broken rotor bar and bearing fault detection. Cluster information obtained by using Independent Component Analysis (ICA) …

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

Research Collection Lee Kong Chian School Of Business

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 …

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, 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 …

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 …

Further Exploration On Relationship Between Crisp Sets And Fuzzy Sets, Zhaoxia Wang, Quan Wang, Ming Bai, Chen Zeng-Qiang, Zhen Sun

Research Collection School Of Computing and Information Systems

This paper employs the concept of fuzzify upgrade operator to describe the relationship between crisp sets, fuzzy sets and higher order fuzzy sets, such as type-2 fuzzy sets. Theory analysis and case studies demonstrate that a crisp set can be represented by a type-1 fuzzy set, a type-1 fuzzy set can be represented by a type-2 fuzzy set, and a type-n (n>=2) fuzzy set can be represented by a type-(n+1) (n>=2) fuzzy set. The fuzzify upgrade operator makes crisp sets, type-1 fuzzy sets, type-2 fuzzy sets and any fuzzy sets much more accessible to all readers. The relationship …

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 …

Software Test Automation, Phillip Laplante, Fevzi Belli, Jerry Gao, Greg Kapfhammer, Keith Miller, W. Wong, Dianxiang Xu

Education Sciences and Professional Programs Faculty Works

No abstract provided.

Task Analysis, Modeling, And Automatic Identification Of Elemental Tasks In Robot-Assisted Laparoscopic Surgery, Lavie Pinchas Golenberg

Wayne State University Dissertations

Robotic microsurgery provides many advantages for surgical operations, including tremor filtration, an increase in dexterity, and smaller incisions. There is a growing need for a task analyses on robotic laparoscopic operations to understand better the tasks involved in robotic microsurgery cases. A few research groups have conducted task observations to help systems automatically identify surgeon skill based on task execution. Their gesture analyses, however, lacked depth and their class libraries were composed of ambiguous groupings of gestures that did not share contextual similarities.

A Hierarchical Task Analysis was performed on a four-throw suturing task using a robotic microsurgical platform. Three …

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 …

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 …

Adaptive Type-2 Fuzzy Maintenance Advisor For Offshore Power Systems, Zhaoxia Wang, C. S. Chang, Fan Yang, W. W. Tan

Research Collection School Of Computing and Information Systems

Proper maintenance strategies are very desirable for minimizing the operational and maintenance costs of power systems without sacrificing reliability. Condition-based maintenance has largely replaced time-based maintenance because of the former's potential economic benefits. As offshore substations are often remotely located, they experience more adverse environments, higher failures, and therefore need more powerful analytical tools than their onshore counterpart. As reliability information collected during operation of an offshore substation can rarely avoid uncertainties, it is essential to obtain consistent estimates of reliability measures under changing environmental and operating conditions. Some attempts with type-1 fuzzy logic were made with limited success in …

Reinforcement-Learning-Based Output-Feedback Control Of Nonstrict Nonlinear Discrete-Time Systems With Application To Engine Emission Control, Peter Shih, Brian C. Kaul, Jagannathan Sarangapani, J. A. Drallmeier

Electrical and Computer Engineering Faculty Research & Creative Works

A novel reinforcement-learning-based output adaptive neural network (NN) controller, which is also referred to as the adaptive-critic NN controller, is developed to deliver the desired tracking performance for a class of nonlinear discrete-time systems expressed in nonstrict feedback form in the presence of bounded and unknown disturbances. The adaptive-critic NN controller consists of an observer, a critic, and two action NNs. The observer estimates the states and output, and the two action NNs provide virtual and actual control inputs to the nonlinear discrete-time system. The critic approximates a certain strategic utility function, and the action NNs minimize the strategic utility …

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.

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 …

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.

Detecting Automotive Exhaust Gas Based On Fuzzy Inference System, Li. Shujin, Ming Bai, Quan Wang, Bo Chen, Xiaobing Zhao, Ting Yang, Zhaoxia Wang

Research Collection School Of Computing and Information Systems

This paper proposes a method of detecting automotive exhaust gas based on fuzzy logic inference after analyzing the principle of the infrared automobile exhaust gas analyzer and the influence of the environmental temperature on analyzer. This paper analyses the measurement error caused by environmental temperature, and then makes a non-linear error correction of temperature for the infrared sensor using fuzzy inference. The results of simulation have clearly demonstrated that the proposed fuzzy compensation scheme is better than the non-fuzzy method.

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 …

Multilayer Image Inpainting Approach Based On Neural Networks, Quan Wang, Zhaoxia Wang, Che Sau Chang, Ting Yang

Research Collection School Of Computing and Information Systems

This paper describes an image inpainting approach based on the self-organizing map for dividing an image into several layers, assigning each damaged pixel to one layer, and then restoring these damaged pixels by the information of their respective layer. These inpainted layers are then fused together to provide the final inpainting results. This approach takes advantage of the neural network's ability of imitating human's brain to separate objects of an image into different layers for inpainting. The approach is promising as clearly demonstrated by the results in this paper.

An Agent-Based Commodity Trading Simulation, Shih-Fen Cheng, Yee Pin Lim

Research Collection School Of Computing and Information Systems

In this paper, an event-centric commodity trading simulation powered by the multiagent framework is presented. The purpose of this simulation platform is for training novice traders. The simulation is progressed by announcing news events that affect various aspects of the commodity supply chain. Upon receiving these events, market agents that play the roles of producers, consumers, and speculators would adjust their views on the market and act accordingly. Their actions would be based on their roles and also their private information, and collectively they shape the market dynamics. This simulation has been effectively deployed for several training sessions. We will …

An Agent-Based Commodity Trading Simulation, Shih-Fen Cheng, Yee Pin Lim, Chao-Chi Liu

Research Collection School Of Computing and Information Systems

In recent years, the study of trading in electronic markets has received significant amount of attention, particularly in the areas of artificial intelligence and electronic commerce. With increasingly sophisticated technologies being applied in analyzing information and making decisions, fully autonomous software agents are expected to take up significant roles in many important fields. This trend is most obvious in the financial domain, where speed of reaction is highly valued and significant investments have been made in information and communication technologies.Despite the successes of automated trading in many important classes of financial markets, commodity trading has lagged behind, mainly because of …

Constraint-Based Dynamic Programming For Decentralized Pomdps With Structured Interactions, Akshat Kumar, Shlomo Zilberstein

Research Collection School Of Computing and Information Systems

Decentralized partially observable MDPs (DEC-POMDPs) provide a rich framework for modeling decision making by a team of agents. Despite rapid progress in this area, the limited scalability of solution techniques has restricted the applicability of the model. To overcome this computational barrier, research has focused on restricted classes of DEC-POMDPs, which are easier to solve yet rich enough to capture many practical problems. We present CBDP, an efficient and scalable point-based dynamic programming algorithm for one such model called ND-POMDP (Network Distributed POMDP). Specifically, CBDP provides magnitudes of speedup in the policy computation and generates better quality solution for all …

Distributed Constraint Optimization With Structured Resource Constraints, Akshat Kumar, Boi Faltings, Adrian Petcu

Research Collection School Of Computing and Information Systems

Distributed constraint optimization (DCOP) provides a framework for coordinated decision making by a team of agents. Often, during the decision making, capacity constraints on agents' resource consumption must be taken into account. To address such scenarios, an extension of DCOP-Resource Constrained DCOP - has been proposed. However, certain type of resources have an additional structure associated with them and exploiting it can result in more efficient algorithms than possible with a general framework. An example of these are distribution networks, where the flow of a commodity from sources to sinks is limited by the flow capacity of edges. We present …