Open Access. Powered by Scholars. Published by Universities.®
Artificial Intelligence and Robotics Commons™
Open Access. Powered by Scholars. Published by Universities.®
- Discipline
-
- Databases and Information Systems (360)
- Engineering (296)
- Operations Research, Systems Engineering and Industrial Engineering (256)
- Business (177)
- Graphics and Human Computer Interfaces (176)
-
- Social and Behavioral Sciences (173)
- Software Engineering (137)
- Numerical Analysis and Scientific Computing (112)
- Theory and Algorithms (103)
- Public Affairs, Public Policy and Public Administration (75)
- Transportation (63)
- Programming Languages and Compilers (62)
- Information Security (43)
- Medicine and Health Sciences (43)
- Technology and Innovation (38)
- OS and Networks (37)
- Education (36)
- Law (36)
- Asian Studies (35)
- Computer Engineering (35)
- International and Area Studies (35)
- Health Information Technology (30)
- Science and Technology Law (22)
- Psychology (21)
- Library and Information Science (20)
- Finance and Financial Management (18)
- Higher Education (18)
- Keyword
-
- Artificial intelligence (98)
- Machine learning (55)
- Reinforcement learning (42)
- Deep learning (38)
- Artificial Intelligence (30)
-
- Large Language Models (30)
- Generative AI (29)
- ChatGPT (23)
- Large Language Model (23)
- Large language models (23)
- Singapore (22)
- Computer vision (19)
- Large language model (18)
- Optimization (18)
- Reinforcement Learning (18)
- Scheduling (18)
- Anomaly detection (17)
- Natural language processing (17)
- Deep reinforcement learning (16)
- Deep Learning (15)
- LLMs (15)
- Machine Learning (15)
- Vehicle routing problem (15)
- AI (14)
- Neural networks (13)
- Uncertainty (13)
- Software engineering (12)
- Graph neural networks (11)
- Metaverse (10)
- Multi-agent systems (10)
- Publication Year
- Publication
-
- Research Collection School Of Computing and Information Systems (1664)
- Dissertations and Theses Collection (Open Access) (57)
- Research Collection Lee Kong Chian School Of Business (33)
- Research Collection Yong Pung How School Of Law (31)
- Research Collection School of Social Sciences (22)
-
- Asian Management Insights (16)
- FORCE 2026 (14)
- Perspectives@SMU (11)
- Research Collection College of Integrative Studies (10)
- Research Collection Library (8)
- PhD Student’s Publications Collection (6)
- MITB Thought Leadership Series (4)
- 2024 AI for Research Week (3)
- CCX Research (3)
- LARC Research Publications (2)
- Research Collection School Of Accountancy (2)
- CASTLe: Collection of Articles on Scholarship for Teaching and Learning (1)
- Centre for AI & Data Governance (2019-2025) (1)
- Centre for Computational Law (2022-2025) (1)
- ROSA Journal Articles and Publications (1)
- Research Collection Office of Research (1)
- Research Collection School Of Economics (1)
- Research@SMU Infographics (1)
- Research@SMU: Connecting the Dots (1)
- SMU Press Releases and News (1)
- Sim Kee Boon Institute for Financial Economics (1)
- Student Publications (1)
- Publication Type
- File Type
Articles 1831 - 1860 of 1897
Full-Text Articles in Artificial Intelligence and Robotics
Iterated Weaker-Than-Weak Dominance, Shih-Fen Cheng, Michael P. Wellman
Iterated Weaker-Than-Weak Dominance, Shih-Fen Cheng, Michael P. Wellman
Research Collection School Of Computing and Information Systems
We introduce a weakening of standard gametheoretic δ-dominance conditions, called dominance, which enables more aggressive pruning of candidate strategies at the cost of solution accuracy. Equilibria of a game obtained by eliminating a δ-dominated strategy are guaranteed to be approximate equilibria of the original game, with degree of approximation bounded by the dominance parameter. We can apply elimination of δ-dominated strategies iteratively, but the for which a strategy may be eliminated depends on prior eliminations. We discuss implications of this order independence, and propose greedy heuristics for determining a sequence of eliminations to reduce the game as far as possible …
Integrating Semantic Templates With Decision Tree For Image Semantic Learning, Ying Liu, Dengsheng Zhang, Guojun Lu, Ah-Hwee Tan
Integrating Semantic Templates With Decision Tree For Image Semantic Learning, Ying Liu, Dengsheng Zhang, Guojun Lu, Ah-Hwee Tan
Research Collection School Of Computing and Information Systems
Decision tree (DT) has great potential in image semantic learning due to its simplicity in implementation and its robustness to incomplete and noisy data. Decision tree learning naturally requires the input attributes to be nominal (discrete). However, proper discretization of continuous-valued image features is a difficult task. In this paper, we present a decision tree based image semantic learning method, which avoids the difficult image feature discretization problem by making use of semantic template (ST) defined for each concept in our database. A ST is the representative feature of a concept, generated from the low-level features of a collection of …
Cosign: A Parallel Algorithm For Coordinated Traffic Signal Control, Shih-Fen Cheng, Marina A. Epelman, Robert L. Smith
Cosign: A Parallel Algorithm For Coordinated Traffic Signal Control, Shih-Fen Cheng, Marina A. Epelman, Robert L. Smith
Research Collection School Of Computing and Information Systems
The problem of finding optimal coordinated signal timing plans for a large number of traffic signals is a challenging problem because of the exponential growth in the number of joint timing plans that need to be explored as the network size grows. In this paper, the game-theoretic paradigm of fictitious play to iteratively search for a coordinated signal timing plan is employed, which improves a system-wide performance criterion for a traffic network. The algorithm is robustly scalable to realistic-size networks modeled with high-fidelity simulations. Results of a case study for the city of Troy, MI, where there are 75 signalized …
Dynamic Multi-Linked Negotiations In Multi-Echelon Production Scheduling Networks, Hoong Chuin Lau, Guan Li Soh, Wee Chong Wan
Dynamic Multi-Linked Negotiations In Multi-Echelon Production Scheduling Networks, Hoong Chuin Lau, Guan Li Soh, Wee Chong Wan
Research Collection School Of Computing and Information Systems
In this paper, we are concerned with scheduling resources in a multi-tier production/logistics system for multi-indenture goods. Unlike classical production scheduling problems, the problem we study is concerned with local utilities which are private. We present an agent model and investigate an efficient scheme for handling multi-linked agent negotiations. With this scheme we attempt to overcome the drawbacks of sequential negotiations and negotiation parameter settings. Our approach is based on embedding a credit-based negotiation protocol within a local search scheduling algorithm. We demonstrate the computational efficiency and effectiveness of the approach in solving a real-life dynamic production scheduling problem which …
Plans As Products Of Learning, Samin Karim, Budhitama Subagdja, Liz Sonenberg
Plans As Products Of Learning, Samin Karim, Budhitama Subagdja, Liz Sonenberg
Research Collection School Of Computing and Information Systems
This paper presents motivations and current related work in the field of plan learning. Additionally, two approaches that achieve plan learning are presented. The two presented approaches are centred on the BDI framework of agency and have particular focus on plans, which, alongside goals, are the means to fulfil intentions in most pragmatic and theoretical realisations of the BDI framework. The first approach is a hybrid architecture that combines a BDI plan extractor and executor with a generic low-level learner. The second approach uses hypotheses to suggest incremental refinements of a priori plans. Both approaches achieve plan generation that is …
Robust Controllability In Temporal Constraint Networks Under Uncertainty, Hoong Chuin Lau, Jia Li, Roland H. C. Yap
Robust Controllability In Temporal Constraint Networks Under Uncertainty, Hoong Chuin Lau, Jia Li, Roland H. C. Yap
Research Collection School Of Computing and Information Systems
Temporal constraint networks are embedded in many planning and scheduling problems. In dynamic problems, a fundamental challenge is to decide whether such a network can be executed as uncertainty is revealed over time. Very little work in this domain has been done in the probabilistic context. In this paper, we propose a Temporal Constraint Network (TCN) model where durations of uncertain activities are represented by random variables. We wish to know whether such a network is robust controllable, i.e. can be executed dynamically within a given failure probability, and if so, how one might find a feasible schedule as the …
Viz: A Visual Analysis Suite For Explaining Local Search Behavior, Steven Halim, Roland H. C. Yap, Hoong Chuin Lau
Viz: A Visual Analysis Suite For Explaining Local Search Behavior, Steven Halim, Roland H. C. Yap, Hoong Chuin Lau
Research Collection School Of Computing and Information Systems
NP-hard combinatorial optimization problems are common in real life. Due to their intractability, local search algorithms are often used to solve such problems. Since these algorithms are heuristic-based, it is hard to understand how to improve or tune them. We propose an interactive visualization tool, VIZ, meant for understanding the behavior of local search. VIZ uses animation of abstract search trajectories with other visualizations which are also animated in a VCR-like fashion to graphically playback the algorithm behavior. It combines generic visualizations applicable on arbitrary algorithms with algorithm and problem specific visualizations. We use a variety of techniques such as …
Two-Instant Reallocation In Two-Echelon Spare Parts Inventory Systems, Huawei Song, Hoong Chuin Lau
Two-Instant Reallocation In Two-Echelon Spare Parts Inventory Systems, Huawei Song, Hoong Chuin Lau
Research Collection School Of Computing and Information Systems
In this paper, we study the problem of deciding when and how to perform reallocation of existing spare parts in a multi-echelon reparable item inventory system. We present a mathematical model that solves the problem when there are two reallocation instants, in response to the open challenge post by Cao and Silver(2005) to consider two or more possible reallocations within a replenishment cycle.
Multi-Learner Based Recursive Supervised Training, Laxmi R. Iyer, Kiruthika Ramanathan, Sheng-Uei Guan
Multi-Learner Based Recursive Supervised Training, Laxmi R. Iyer, Kiruthika Ramanathan, Sheng-Uei Guan
Research Collection School Of Computing and Information Systems
In this paper, we propose the multi-learner based recursive supervised training (MLRT) algorithm, which uses the existing framework of recursive task decomposition, by training the entire dataset, picking out the best learnt patterns, and then repeating the process with the remaining patterns. Instead of having a single learner to classify all datasets during each recursion, an appropriate learner is chosen from a set of three learners, based on the subset of data being trained, thereby avoiding the time overhead associated with the genetic algorithm learner utilized in previous approaches. In this way MLRT seeks to identify the inherent characteristics of …
Fuzzy Cognitive Goal Net For Interactive Storytelling Plot Design, Yundong Cai, Chunyan Miao, Ah-Hwee Tan, Zhiqi Shen
Fuzzy Cognitive Goal Net For Interactive Storytelling Plot Design, Yundong Cai, Chunyan Miao, Ah-Hwee Tan, Zhiqi Shen
Research Collection School Of Computing and Information Systems
Interactive storytelling attracts a lot of research interests among the interactive entertainments in recent years. Designing story plot for interactive storytelling is currently one of the most critical problems of interactive storytelling. Some traditional AI planning methods, such as Hierarchical Task Network, Heuristic Searching Method are widely used as the planning tool for the story plot design. This paper proposes a model called Fuzzy Cognitive Goal Net as the story plot planning tool for interactive storytelling, which combines the planning capability of Goal net and reasoning ability of Fuzzy Cognitive Maps. Compared to conventional methods, the proposed model shows a …
Winning Back The Cup For Distributed Pomdps: Planning Over Continuous Belief Spaces, Pradeep Varakantham, Ranjit Nair, Milind Tambe, Makoto Yokoo
Winning Back The Cup For Distributed Pomdps: Planning Over Continuous Belief Spaces, Pradeep Varakantham, Ranjit Nair, Milind Tambe, Makoto Yokoo
Research Collection School Of Computing and Information Systems
Distributed Partially Observable Markov Decision Problems (Distributed POMDPs) are evolving as a popular approach for modeling multiagent systems, and many different algorithms have been proposed to obtain locally or globally optimal policies. Unfortunately, most of these algorithms have either been explicitly designed or experimentally evaluated assuming knowledge of a starting belief point, an assumption that often does not hold in complex, uncertain domains. Instead, in such domains, it is important for agents to explicitly plan over continuous belief spaces. This paper provides a novel algorithm to explicitly compute finite horizon policies over continuous belief spaces, without restricting the space of …
Evaluation Of Time-Varying Availability In Multi-Echelon Spare Parts Systems With Passivation, Hoong Chuin Lau, Huawei Song, Chuen Teck See, Siew Yen Cheng
Evaluation Of Time-Varying Availability In Multi-Echelon Spare Parts Systems With Passivation, Hoong Chuin Lau, Huawei Song, Chuen Teck See, Siew Yen Cheng
Research Collection School Of Computing and Information Systems
The popular models for repairable item inventory, both in the literature as well as practical applications, assume that the demands for items are independent of the number of working systems. However this assumption can introduce a serious underestimation of availability when the number of working systems is small, the failure rate is high or the repair time is long. In this paper, we study a multi-echelon repairable item inventory system under the phenomenon of passivation, i.e. serviceable items are passivated (“switched off”) upon system failure. This work is motivated by corrective maintenance of high-cost technical equipment in the miltary. We …
Transition Rule Elicitation Methods For Urban Cellular Automata Models, Junfeng Jiao, Luc Boerboom
Transition Rule Elicitation Methods For Urban Cellular Automata Models, Junfeng Jiao, Luc Boerboom
Research Collection College of Integrative Studies
In this chapter, transition rules used in urban CA models are reviewed and classified into two categories: transition potential rules and conflict resolution rules. Then, four widely used rule elicitation methods: Regression analysis, Artificial Neural network (ANN), Visual calibration, and Analytical Hierarchy Processing – Multi Criteria Evaluation (AHP-MCE) are discussed. Most of these methods are data driven methods and can be used to elicit the transition potential rules in the urban CA models. In the following, three possible rule elicitation methods: Interview, Document analysis, and Card sorting are explained and demonstrated. These three methods are driven by knowledge and can …
Visualization For Analyzing Trajectory-Based Metaheuristic Search Algorithms, Steven Halim, Roland H. C. Yap, Hoong Chuin Lau
Visualization For Analyzing Trajectory-Based Metaheuristic Search Algorithms, Steven Halim, Roland H. C. Yap, Hoong Chuin Lau
Research Collection School Of Computing and Information Systems
No abstract provided.
Multiagent Teamwork: Hybrid Approaches, Praveen Paruchuri, Emma Bowring, Ranjit Nair, Jonathan Pearce, Nathan Schurr, Milind Tambe, Pradeep Varakantham
Multiagent Teamwork: Hybrid Approaches, Praveen Paruchuri, Emma Bowring, Ranjit Nair, Jonathan Pearce, Nathan Schurr, Milind Tambe, Pradeep Varakantham
Research Collection School Of Computing and Information Systems
Today within the multiagent community, we see at least four competing methods to building multiagent systems: beliefdesireintention (BDI), distributed constraint optimization (DCOP), distributed POMDPs, and auctions or game-theoretic methods. While there is exciting progress within each approach, there is a lack of cross-cutting research. This article highlights the various hybrid techniques for multiagent teamwork developed by the teamcore group. In particular, for the past decade, the TEAMCORE research group has focused on building agent teams in complex, dynamic domains. While our early work was inspired by BDI, we will present an overview of recent research that uses DCOPs and distributed …
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 …
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 …
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.
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 …
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 …
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 …
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.
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 …
Human Mental Models Of Humanoid Robots, Sau-Lai Lee, Ivy Yee-Man Lau, Sara Kiesler, Chi-Yue Chiu
Human Mental Models Of Humanoid Robots, Sau-Lai Lee, Ivy Yee-Man Lau, Sara Kiesler, Chi-Yue Chiu
Research Collection School of Social Sciences
Effective communication between a person and a robot may depend on whether there exists a common ground of understanding between the two. In two experiments modelled after human-human studies we examined how people form a mental model of a robot's factual knowledge. Participants estimated the robot's knowledge by extrapolating from their own knowledge and from information about the robot's origin and language. These results suggest that designers of humanoid robots must attend not only to the social cues that robots emit but also to the information people use to create mental models of a robot.
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.
Robust Temporal Constraint Networks, Hoong Chuin Lau, Thomas Ou, Melvyn Sim
Robust Temporal Constraint Networks, Hoong Chuin Lau, Thomas Ou, Melvyn Sim
Research Collection School Of Computing and Information Systems
In this paper, we propose the Robust Temporal Constraint Network (RTCN) model for simple temporal constraint networks where activity durations are bounded by random variables. The problem is to determine whether such temporal network can be executed with failure probability less than a given 0 ≤ E ≤ 1 for each possible instantiation of the random variables, and if so. how one might find a feasible schedule with each given instantiation. The advantage of our model is that one can vary the value of ∊ to control the level of conservativeness of the solution. We present a computationally tractable and …
Justilm: Few-Shot Justification Generation For Explainable Fact-Checking Of Real-World Claims, Fengzhu Zeng, Wei Gao
Justilm: Few-Shot Justification Generation For Explainable Fact-Checking Of Real-World Claims, Fengzhu Zeng, Wei Gao
Research Collection School Of Computing and Information Systems
Justification is an explanation that supports the verdict assigned to a claim in fact-checking. However, the task of justification generation is previously oversimplified as summarization of fact-check article authored by professional checkers. In this work, we propose a realistic approach to generate justification based on retrieved evidence. We present a new benchmark dataset called ExClaim for Explainable Claim verification, and introduce JustiLM, a novel few-shot retrieval-augmented language model to learn justification generation by leveraging fact-check articles as auxiliary resource during training. Our results show that JustiLM outperforms in-context learning (ICL)-enabled LMs including Flan-T5 and Llama2, and the retrieval-augmented model Atlas …
Two-Echelon Repairable Item Inventory System With Limited Repair Capacity Under Nonstationary Demands, Hoong Chuin Lau, Huawei Song
Two-Echelon Repairable Item Inventory System With Limited Repair Capacity Under Nonstationary Demands, Hoong Chuin Lau, Huawei Song
Research Collection School Of Computing and Information Systems
We study a repairable item inventory system under limited repair capacity and nonstationary Poisson demands, motivated by corrective maintenance of military equipment. Our goal is to minimize the cost of both spare and repair resource allocation. We propose an efficient analytical model that combines optimization modeling and queuing theory.
Corrective Maintenance Optimization In An Air Force, Hoong Chuin Lau, K. Y. Neo, W. C. Wan
Corrective Maintenance Optimization In An Air Force, Hoong Chuin Lau, K. Y. Neo, W. C. Wan
Research Collection School Of Computing and Information Systems
Successful military mission planning and execution depend critically on equipment serviceability and resupply. Due to the stochastic nature of demands, the forecast of optimal spares and resources needed to guarantee the level of serviceability is a complex problem, especially in a multi-echelon setting. In this paper, we propose a decision-support concept and software tool known as Corrective Maintenance Optimizer (CMO) that helps to optimize system availability, through proper allocation of spare parts, both strategically and operationally.