Academic literature on the topic 'Dynamic REsource Allocation with Multi-task assignment'
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Journal articles on the topic "Dynamic REsource Allocation with Multi-task assignment"
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Hasheminezhad, Mahdi, and Ardeshir Bahreininejad. "A Multi-Agent Taxi Dispatching System." International Journal of Agent Technologies and Systems 2, no. 2 (April 2010): 1–10. http://dx.doi.org/10.4018/jats.2010040101.
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The taxi assignment problem may be categorized as a vehicle routing problem. ?When placed in the field of resource allocation, it is a dynamic problem in which ?the situation changes as the work progresses. This paper presents a new agent-based approach to tackle the taxi assignment problem. New parameters are ?introduced to increase the satisfaction of the drivers. The authors propose a new algorithm to improve the parameters. Simulations were also conducted to examine the efficiency of the proposed method. The results indicate the effectiveness of the proposed taxi assignment/dispatching approach.
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Leng, Tao, Xiaoyao Li, Dongwei Hu, Gaofeng Cui, and Weidong Wang. "Collaborative Computing and Resource Allocation for LEO Satellite-Assisted Internet of Things." Wireless Communications and Mobile Computing 2021 (September 15, 2021): 1–12. http://dx.doi.org/10.1155/2021/4212548.
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Satellite-assisted internet of things (S-IoT), especially the S-IoT based on low earth orbit (LEO) satellite, plays an important role in future wireless systems. However, the limited on-board communication and computing resource and high mobility of LEO satellites make it hard to provide satisfied service for IoT users. To maximize the task completion rate under latency constraints, collaborative computing and resource allocation among LEO networks are jointly investigated in this paper, and the joint task offloading, scheduling, and resource allocation is formulated as a dynamic mixed-integer problem. To tack the complex problem, we decouple it into two subproblems with low complexity. First, the max-min fairness is adopted to minimize the maximum latency via optimal resource allocation with fixed task assignment. Then, the joint task offloading and scheduling is formulated as a Markov decision process with optimal communication and computing resource allocation, and deep reinforcement learning is utilized to obtain long-term benefits. Simulation results show that the proposed scheme has superior performance compared with other referred schemes.
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Robu, V., E. H. Gerding, S. Stein, D. C. Parkes, A. Rogers, and N. R. Jennings. "An Online Mechanism for Multi-Unit Demand and its Application to Plug-in Hybrid Electric Vehicle Charging." Journal of Artificial Intelligence Research 48 (October 23, 2013): 175–230. http://dx.doi.org/10.1613/jair.4064.
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We develop an online mechanism for the allocation of an expiring resource to a dynamic agent population. Each agent has a non-increasing marginal valuation function for the resource, and an upper limit on the number of units that can be allocated in any period. We propose two versions on a truthful allocation mechanism. Each modifies the decisions of a greedy online assignment algorithm by sometimes cancelling an allocation of resources. One version makes this modification immediately upon an allocation decision while a second waits until the point at which an agent departs the market. Adopting a prior-free framework, we show that the second approach has better worst-case allocative efficiency and is more scalable. On the other hand, the first approach (with immediate cancellation) may be easier in practice because it does not need to reclaim units previously allocated. We consider an application to recharging plug-in hybrid electric vehicles (PHEVs). Using data from a real-world trial of PHEVs in the UK, we demonstrate higher system performance than a fixed price system, performance comparable with a standard, but non-truthful scheduling heuristic, and the ability to support 50% more vehicles at the same fuel cost than a simple randomized policy.
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Fu, Yanming, Yuming Shen, and Liang Tang. "A Dynamic Task Allocation Framework in Mobile Crowd Sensing with D3QN." Sensors 23, no. 13 (July 1, 2023): 6088. http://dx.doi.org/10.3390/s23136088.
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With the coverage of sensor-rich smart devices (smartphones, iPads, etc.), combined with the need to collect large amounts of data, mobile crowd sensing (MCS) has gradually attracted the attention of academics in recent years. MCS is a new and promising model for mass perception and computational data collection. The main function is to recruit a large group of participants with mobile devices to perform sensing tasks in a given area. Task assignment is an important research topic in MCS systems, which aims to efficiently assign sensing tasks to recruited workers. Previous studies have focused on greedy or heuristic approaches, whereas the MCS task allocation problem is usually an NP-hard optimisation problem due to various resource and quality constraints, and traditional greedy or heuristic approaches usually suffer from performance loss to some extent. In addition, the platform-centric task allocation model usually considers the interests of the platform and ignores the feelings of other participants, to the detriment of the platform’s development. Therefore, in this paper, deep reinforcement learning methods are used to find more efficient task assignment solutions, and a weighted approach is adopted to optimise multiple objectives. Specifically, we use a double deep Q network (D3QN) based on the dueling architecture to solve the task allocation problem. Since the maximum travel distance of the workers, the reward value, and the random arrival and time sensitivity of the sensing tasks are considered, this is a dynamic task allocation problem under multiple constraints. For dynamic problems, traditional heuristics (eg, pso, genetics) are often difficult to solve from a modeling and practical perspective. Reinforcement learning can obtain sub-optimal or optimal solutions in a limited time by means of sequential decision-making. Finally, we compare the proposed D3QN-based solution with the standard baseline solution, and experiments show that it outperforms the baseline solution in terms of platform profit, task completion rate, etc., the utility and attractiveness of the platform are enhanced.
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Abbass, Waseem, Riaz Hussain, Jaroslav Frnda, Nasim Abbas, Muhammad Awais Javed, and Shahzad A. Malik. "Resource Allocation in Spectrum Access System Using Multi-Objective Optimization Methods." Sensors 22, no. 4 (February 9, 2022): 1318. http://dx.doi.org/10.3390/s22041318.
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The paradigm of dynamic shared access aims to provide flexible spectrum usage. Recently, Federal Communications Commission (FCC) has proposed a new dynamic spectrum management framework for the sharing of a 3.5 GHz (3550–3700 MHz) federal band, called a citizen broadband radio service (CBRS) band, which is governed by spectrum access system (SAS). It is the responsibility of SAS to manage the set of CBRS-SAS users. The set of users are classified in three tiers: incumbent access (IA) users, primary access license (PAL) users and the general authorized access (GAA) users. In this article, dynamic channel assignment algorithm for PAL and GAA users is designed with the goal of maximizing the transmission rate and minimizing the total cost of GAA users accessing PAL reserved channels. We proposed a new mathematical model based on multi-objective optimization for the selection of PAL operators and idle PAL reserved channels allocation to GAA users considering the diversity of PAL reserved channels’ attributes and the diversification of GAA users’ business needs. The proposed model is estimated and validated on various performance metrics through extensive simulations and compared with existing algorithms such as Hungarian algorithm, auction algorithm and Gale–Shapley algorithm. The proposed model results indicate that overall transmission rate, net cost and data-rate per unit cost remain the same in comparison to the classical Hungarian method and auction algorithm. However, the improved model solves the resource allocation problem approximately up to four times faster with better load management, which validates the efficiency of our model.
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Zayas-Cabán, Gabriel, and Hyun-Soo Ahn. "DYNAMIC CONTROL OF A SINGLE-SERVER SYSTEM WHEN JOBS CHANGE STATUS." Probability in the Engineering and Informational Sciences 32, no. 3 (June 7, 2017): 353–95. http://dx.doi.org/10.1017/s0269964817000213.
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From health care to maintenance shops, many systems must contend with allocating resources to customers or jobs whose initial service requirements or costs change when they wait too long. We present a new queueing model for this scenario and use a Markov decision process formulation to analyze assignment policies that minimize holding costs. We show that the classic cμ rule is generally not optimal when service or cost requirements can change. Even for a two-class customer model where a class 1 task becomes a class 2 task upon waiting, we show that additional orderings of the service rates are needed to ensure the optimality of simple priority rules. We then show that seemingly-intuitive switching curve structures are also not optimal in general. We study these scenarios and provide conditions under which they do hold. Lastly, we show that results from the two-class model do not extend to when there are n≥3 customer classes. More broadly, we find that simple priority rules are not optimal. We provide sufficient conditions under which a simple priority rule holds. In short, allowing service and/or cost requirements to change fundamentally changes the structure of the optimal policy for resource allocation in queueing systems.
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Behera, Sasmita Rani, Niranjan Panigrahi, Sourav Kumar Bhoi, Kshira Sagar Sahoo, N. Z. Jhanjhi, and Rania M. Ghoniem. "Time Series-Based Edge Resource Prediction and Parallel Optimal Task Allocation in Mobile Edge Computing Environment." Processes 11, no. 4 (March 27, 2023): 1017. http://dx.doi.org/10.3390/pr11041017.
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The offloading of computationally intensive tasks to edge servers is indispensable in the mobile edge computing (MEC) environment. Once the tasks are offloaded, the subsequent challenges lie in buffering them and assigning them to edge virtual machine (VM) resources to meet the multicriteria requirement. Furthermore, the edge resources’ availability is dynamic in nature and needs a joint prediction and optimal allocation for the efficient usage of resources and fulfillment of the tasks’ requirements. To this end, this work has three contributions. First, a delay sensitivity-based priority scheduling (DSPS) policy is presented to schedule the tasks as per their deadline. Secondly, based on exploratory data analysis and inferred seasonal patterns in the usage of edge CPU resources from the GWA-T-12 Bitbrains VM utilization dataset, the availability of VM resources is predicted by using a Holt–Winters-based univariate algorithm (HWVMR) and a vector autoregression-based multivariate algorithm (VARVMR). Finally, for optimal and fast task assignment, a parallel differential evolution-based task allocation (pDETA) strategy is proposed. The proposed algorithms are evaluated extensively with standard performance metrics, and the results show nearly 22%, 35%, and 69% improvements in cost and 41%, 52%, and 78% improvements in energy when compared with MTSS, DE, and min–min strategies, respectively.
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Si, JiaShuai, and MingRui Hao. "Online Weapon-target Assignment based on Distributed Auction Mechanism." Journal of Physics: Conference Series 2456, no. 1 (March 1, 2023): 012044. http://dx.doi.org/10.1088/1742-6596/2456/1/012044.
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Abstract To solve the problem of online weapon-target assignment (OWTA) in the integration of large-scale search and attack in unknown environment, an OWTA algorithm based on distributed auction mechanism is presented. Aiming at the problem that the traditional combinatorial optimization algorithm needs to set up the global battlefield situation in advance, considering the consumability of resources in the attack process, the integrated search and attack task flow is established. Considering the communication restricted environment, the unmanned aerial vehicles (uavs) are grouped, with centralized architecture within the group and distributed structure between the groups, and the corresponding distributed auction mechanism is constructed to achieve OWTA within the communication range limited. In order to solve the problem that it is difficult to ensure the time consistency of the coordinated attack target, a dubins cooperative path planning based on cooperative particle swarm optimization (CPSO) algorithm is proposed. Particle swarm optimization algorithm is used to adjust the radius of the dubins path of each bomb, so that the uav in the same group can hit the target simultaneously without collision and have the shortest flight range. The simulation results show that the designed distributed auction algorithm takes into account the consumption of attack resources, and quickly redistributes the firepower to the new targets in the dynamic uncertain environment, which ensures the maximization of the execution efficiency of the multi-machine cluster fire allocation task.
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Alkanhel, Reem, Ahsan Rafiq, Evgeny Mokrov, Abdukodir Khakimov, Mohammed Saleh Ali Muthanna, and Ammar Muthanna. "Enhanced Slime Mould Optimization with Deep-Learning-Based Resource Allocation in UAV-Enabled Wireless Networks." Sensors 23, no. 16 (August 10, 2023): 7083. http://dx.doi.org/10.3390/s23167083.
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Unmanned aerial vehicle (UAV) networks offer a wide range of applications in an overload situation, broadcasting and advertising, public safety, disaster management, etc. Providing robust communication services to mobile users (MUs) is a challenging task because of the dynamic characteristics of MUs. Resource allocation, including subchannels, transmit power, and serving users, is a critical transmission problem; further, it is also crucial to improve the coverage and energy efficacy of UAV-assisted transmission networks. This paper presents an Enhanced Slime Mould Optimization with Deep-Learning-based Resource Allocation Approach (ESMOML-RAA) in UAV-enabled wireless networks. The presented ESMOML-RAA technique aims to efficiently accomplish computationally and energy-effective decisions. In addition, the ESMOML-RAA technique considers a UAV as a learning agent with the formation of a resource assignment decision as an action and designs a reward function with the intention of the minimization of the weighted resource consumption. For resource allocation, the presented ESMOML-RAA technique employs a highly parallelized long short-term memory (HP-LSTM) model with an ESMO algorithm as a hyperparameter optimizer. Using the ESMO algorithm helps properly tune the hyperparameters related to the HP-LSTM model. The performance validation of the ESMOML-RAA technique is tested using a series of simulations. This comparison study reports the enhanced performance of the ESMOML-RAA technique over other ML models.
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D. Suresh, S. Satheesbabu, P. Gokulakrishnan,. "ONLINE PURCHASING PLATFORM USING CROWD SOURCING WITH IMPROVISATION OF CLASSIFICATION ACCURACY." INFORMATION TECHNOLOGY IN INDUSTRY 9, no. 1 (March 17, 2021): 1123–34. http://dx.doi.org/10.17762/itii.v9i1.246.
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Crowd-sourcing is a prototype where persons cum organisations acquire facts such as ideas, micro-tasks, financial, vote casting associated to items and offerings from individuals of large, open and rapidly-evolving nature. It entails utilization of web acquired and distribute work between members to get a collective result. The software of classification tasks in crowd-sourcing is a counter step due to the inclined reputation of crowd-sourcing market. Dynamic Label Acquisition and Answer Aggregation (DLTA) crowd-sourcing framework accomplishes the classification assignment in a promising manner. But most of the current works are now not in a position to supply an budget allocation for labels due to the fact they do not make the most the Label inference and acquisition phase. In addition, label mismatch and multi-label tasks are the different issues encountered in the current works. To overcome, it is proposed to undertake Random Forest Algorithm (RFA) for classification in crowd-sourcing. The goal of this work is to enhance the crowd-sourcing classification task efficiency with Dynamic Resource Algorithm. RFA is activated by means of developing a multitude of decision tree at training time and consequences with the training and it applies a bagging approach to produce the last end result with more accuracy.
Dissertations / Theses on the topic "Dynamic REsource Allocation with Multi-task assignment"
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Griffin, Jacqueline A. "Improving health care delivery through multi-objective resource allocation." Diss., Georgia Institute of Technology, 2012. http://hdl.handle.net/1853/50108.
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This dissertation addresses resource allocation problems that occur in both public and private health care settings with the objective of characterizing the tradeoffs that occur when simultaneously incorporating multiple objectives and developing methods to address these tradeoffs. We examine three resource allocation problems (i) strategic allocation of financial resources and limited staffing capacity for the mobile delivery of health care within African countries, (ii) real-time allocation of hospital beds to internal patient requests, and (iii) development of patient redirection policies in response to limited bed availability in units within a system of hospitals. For each problem we define models, each with a different methodology, and utilize the models to develop allocation strategies that account for multiple competing objectives and examine the performance of the strategies with computational studies. In Chapter 2, we model African health care delivery systems utilizing a mixed-integer program (MIP) which accounts for financial and personnel constraints as well as infrastructure quality. We characterize tradeoffs in effectiveness, efficiency, and equity resulting from four allocation strategies with computational experiments representing the variety of spatial patterns that occur throughout the continent. The main contributions include (i) the development of a model that incorporates spatial and infrastructure characteristics and allows for a study of equity in the delivery of care, rather than access to care, and (ii) the characterization of tradeoffs in the three objectives under a variety of settings. In Chapter 3, we model the real-time assignment of bed requests to available beds as a queueing system and a Markov decision process (MDP). Through the development of bed assignment algorithms and simulation experiments, we illustrate the value of implementing strategic bed assignment practices which balance the bed management objectives of timeliness and appropriateness of assignments. The main contributions of this section include (i) the development of new bed assignment algorithms which use stochastic optimization techniques and outperform algorithms which mimic processes currently used in practice and (ii) the definition of a model and methods for the control of a large complex system that includes flexible units, multiple patient types, and type-dependent routing. In Chapter 4, we model the impact of a patient redirection policy in a hospital unit as a Markov chain. Assuming preferences for patient redirection are aligned with costs, we examine the impact of incremental changes to redirection policies on the probability of the unit being completely occupied, the long-run average utilization, and the long-run average cost of redirection. The main contributions of this chapter include (i) the introduction of a model of patient redirection with multiple patient thresholds and patient preference constraints and (ii) the definition of necessary conditions for an optimal patient redirection policy that minimizes the average cost of redirection.
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Velhal, Shridhar. "Development of Spatio-Temporal Multi-Task Assignment Approaches for Perimeter Defense Problem." Thesis, 2023. https://etd.iisc.ac.in/handle/2005/6196.
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Rapidly evolving technologies in the autonomous operation of Uninhabited Aerial Vehicles (UAVs) and associated developments in low-cost sensors have created significant interest among researchers in using them for various civil and military applications. With the autonomy and presence of various sensing equipment, onboard UAVs lead to problems in the privacy, safety, and security of many safety-critical infrastructures. A critical infrastructure that needs to be protected is approximated by the convex region and called territory. A team of UAVs that protects the territory is called the defenders and UAVs which try to enter the territory are called the intruders. A team of defenders operates inside and, on the perimeter, and protects the territory from intruders by capturing intruders on the perimeter is referred as the Perimeter Defense Problem (PDP). The velocity of intruders is used to predict the arrival location on the perimeter and arrival time. In this way, each intruder generates a spatio-temporal task for the defenders to reach tha= t specific location at a specific time to neutralize that intruder. So, PDP is formulated as the spatio-temporal multi-task assignment (STMTA) problem. In the STMTA problem, some minimum number of defenders (robots) are required to execute the given spatio-temporal tasks; without this minimum number of defenders, STMTA problem is ill-posed. The proposed Dynamic REsource Allocation with Multi-task assignment (DREAM) algorithm solves the bottleneck issue of iterative computation for the required number of robots and provides the two-step solution to compute the required minimum number of robots and their optimal assignments to execute given spatio-temporal tasks. Next, the trajectory generation algorithm has been developed to compute the trajectory of each defender. Furthermore, it is proved that all the computed trajectories of homogeneous agents, operating in the convex region, are collision-free.
For highly maneuvering intruders, the errors in the prediction of tasks deteriorate the performance of DREAM. In the P-DREAM approach, a dedicated defender is assigned to each prioritized intruder by enforcing the prioritized intruder as a first task. A prioritized intruder must be delegated to the reserve defender before it becomes infeasible for the reserve defender. The static design for PDP computes the minimum number of reserve stations, their optimal location, priority region, monitoring region, and the minimum number of defenders required for monitoring. The quantification of priority and monitoring region will be helpful in practical implementations. For protecting a large territory, more defenders are required, also each defender has a limited sensing range to detect and track intruders. To address these issues of partial observability and scalability the decentralized spatio-temporal multi-task assignment approach is proposed. A modified consensus-based bundle algorithm has been proposed to solve the STMTA problem. Finally, the thesis demonstrates the working of the DREAM approach for heterogeneous pick-up and just-in-time delivery (PJITD) problems. Just-in-time tasks have been used to improve operational efficacy for static (priorly known) tasks. The non-iterative solution of modified DREAM overcomes the bottleneck problem of the iterative (and hence offline) solution and provides a real-time implementable solution. The cost function is modified to include the traveling time, operating time, and heterogeneous skills required to execute the tasks. The working of modified DREAM is illustrated using high-fidelity ROS2-GAZEBO simulations and lab-scale hardware experiments.
Book chapters on the topic "Dynamic REsource Allocation with Multi-task assignment"
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Wang, Bo, and Mingchu Li. "Resource Allocation Scheduling Algorithm Based on Incomplete Information Dynamic Game for Edge Computing." In Research Anthology on Edge Computing Protocols, Applications, and Integration, 414–39. IGI Global, 2022. http://dx.doi.org/10.4018/978-1-6684-5700-9.ch021.
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With the advent of the 5G era, the demands for features such as low latency and high concurrency are becoming increasingly significant. These sophisticated new network applications and services require huge gaps in network transmission bandwidth, network transmission latency, and user experience, making cloud computing face many technical challenges in terms of applicability. In response to cloud computing's shortcomings, edge computing has come into its own. However, many factors affect task offloading and resource allocation in the edge computing environment, such as the task offload latency, energy consumption, smart device mobility, end-user power, and other issues. This paper proposes a dynamic multi-winner game model based on incomplete information to solve multi-end users' task offloading and edge resource allocation. First, based on the history of end-users storage in edge data centers, a hidden Markov model can predict other end-users' bid prices at time t. Based on these predicted auction prices, the model determines their bids. A dynamic multi-winner game model is used to solve the offload strategy that minimizes latency, energy consumption, cost, and to maximizes end-user satisfaction at the edge data center. Finally, the authors designed a resource allocation algorithm based on different priorities and task types to implement resource allocation in edge data centers. To ensure the prediction model's accuracy, the authors also use the expectation-maximization algorithm to learn the model parameters. Comparative experimental results show that the proposed model can better results in time delay, energy consumption, and cost.
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Dumas, J. Ann. "WSIS Gender and ICT Policy." In Information Communication Technologies, 496–503. IGI Global, 2008. http://dx.doi.org/10.4018/978-1-59904-949-6.ch034.
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The World Summit on the Information Society (WSIS) was organized by the United Nations (UN) and the International Telecommunications Union to address the need for international policy and agreement on ICT governance, rights, and responsibilities. It convened in two phases: Geneva in 2003 and Tunis in 2005. International representatives of governments, businesses, and civil society raised issues, and debated and formed policy recommendations. The WSIS Gender Caucus (2003) and other civil-society participants advocated for gender equality to be included as a fundamental principle for action and decision making. The voting plenary session of delegates produced the WSIS Declaration of Principles (UN, 2003a) and WSIS Plan of Action (UN, 2003b) in Geneva, with gender included in many of the articles. Two major issues WSIS addressed in Geneva and Tunis were Internet governance and the Digital Solidarity Fund. UN secretary general Kofi Annan established the Working Group on Internet Governance (WGIG) to define Internet and Internet governance to “navigate the complex terrain” (GKP, 2002, p. 6) and to make recommendations for WSIS in Tunis in 2005. WGIG addressed three Internet-governance functions: technical standardization; resources allocation and assignment, such as domain names; and policy formation and enforcement, and dispute resolution. Relevant issues not initially addressed by WGIG included gender, voice, inclusiveness, and other issues rooted in unequal access to ICT and to the decision-making process including governance, now shaping the information society. On February 23, a joint statement on Internet governance was presented in Geneva at the Tunis Prepcom by the Civil Society Internet Governance Caucus, the Gender Caucus, Human Rights Caucus, Privacy Caucus, and Media Caucus on behalf of the Civil Society Content and Themes Group. The statement asserts, “gender balanced representation in all aspects of Internet Governance is vital for the process and for its outcomes to have legitimacy” (WSIS Gender Caucus, 2005a). The Digital Solidarity Fund was proposed at WSIS, and the UN Task Force on Financial Mechanisms for ICT for Development was formed. In the 1990s, official development-assistance (ODA) support declined for ICT infrastructure development. In the new millennium, this decline has been offset by funds to integrate ICT programs into development (Hesselbarth & Tambo, 2005). The WSIS Gender Caucus (2003) statement on financing mechanisms affirmed that ICT for development must be framed as a development issue, “encompassing market-led growth but fundamentally a public policy issue.” Public finance is central to achieving “equitable and gender just outcomes in ICT for development.” This article examines the WSIS political dynamics over the issue of gender equality as a fundamental principle for action in ICT policy. The WSIS civil-society participants, particularly the Gender Caucus, continued to advocate for gender equality as a fundamental principle for action and decision making within the multiple-stakeholder WSIS process of government delegates and private-sector representatives.
Conference papers on the topic "Dynamic REsource Allocation with Multi-task assignment"
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Merluzzi, Mattia, Paolo Di Lorenzo, and Sergio Barbarossa. "Dynamic Joint Resource Allocation and User Assignment in Multi-access Edge Computing." In ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2019. http://dx.doi.org/10.1109/icassp.2019.8683499.
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Xu, Kai, Kaiming Xiao, Quanjun Yin, Yabing Zha, and Cheng Zhu. "Bridging the Gap between Observation and Decision Making: Goal Recognition and Flexible Resource Allocation in Dynamic Network Interdiction." In Twenty-Sixth International Joint Conference on Artificial Intelligence. California: International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/625.
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Goal recognition, which is the task of inferring an agent’s goals given some or all of the agent’s observed actions, is one of the important approaches in bridging the gap between the observation and decision making within an observe-orient-decide-act cycle. Unfortunately, few researches focus on how to improve the utilization of knowledge produced by a goal recognition system. In this work, we propose a Markov Decision Process-based goal recognition approach tailored to a dynamic shortest-path local network interdiction (DSPLNI) problem. We first introduce a novel DSPLNI model and its solvable dual form so as to incorporate real-time knowledge acquired from goal recognition system. Then a Markov Decision Process-based goal recognition model along with its dynamic Bayesian network representation and the applied goal inference method is proposed to identify the evader’s real goal within the DSPLNI context. Based on that, we further propose an efficient scalable technique in maintaining action utility map used in fast goal inference, and develop a flexible resource assignment mechanism in DSPLNI using knowledge from goal recognition system. Experimental results show the effectiveness and accuracy of our methods both in goal recognition and dynamic network interdiction.
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Nam, Changjoo, and Dylan A. Shell. "Assignment algorithms for modeling resource contention and interference in multi-robot task-allocation." In 2014 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2014. http://dx.doi.org/10.1109/icra.2014.6907156.
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Wang, Pengfei, Zijie Zheng, Boya Di, and Lingyang Song. "Joint Task Assignment and Resource Allocation in the Heterogeneous Multi-Layer Mobile Edge Computing Networks." In GLOBECOM 2019 - 2019 IEEE Global Communications Conference. IEEE, 2019. http://dx.doi.org/10.1109/globecom38437.2019.9014309.
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