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In many practical applications, decision-making processes must balance the costs of acquiring information with the benefits it provides. Traditional control systems often assume full observability, an unrealistic assumption when…

Artificial Intelligence · Computer Science 2025-01-24 Taiyi Wang , Jianheng Liu , Bryan Lee , Zhihao Wu , Yu Wu

The intent of this research is to generate a set of non-dominated policies from which one of two agents (the leader) can select a most preferred policy to control a dynamic system that is also affected by the control decisions of the other…

Optimization and Control · Mathematics 2014-04-18 Yanling Chang , Alan L. Erera , Chelsea C. White

We consider a distributionally robust Partially Observable Markov Decision Process (DR-POMDP), where the distribution of the transition-observation probabilities is unknown at the beginning of each decision period, but their realizations…

Optimization and Control · Mathematics 2020-12-09 Hideaki Nakao , Ruiwei Jiang , Siqian Shen

The recently announced Super Wi-Fi Network proposal in United States is aiming to enable Internet access in a nation-wide area. As traditional cable-connected power supply system becomes impractical or costly for a wide range wireless…

Information Theory · Computer Science 2020-08-05 Tingwu Wang , Jian Wang , Chunxiao Jiang , Jingjing Wang , Yong Ren

Cell-free massive multi-input multi-output (CFmMIMO) offers uniform service quality through distributed access points (APs), yet unresolved issues remain. This paper proposes a heterogeneous system design that goes beyond the original…

Information Theory · Computer Science 2024-08-16 Wei Jiang , Hans D. Schotten

This work addresses the challenge of a robot using real-time feedback from contact sensors to reliably manipulate a movable object on a cluttered tabletop. We formulate contact manipulation as a partially observable Markov decision process…

Robotics · Computer Science 2016-05-03 Michael C. Koval , David Hsu , Nancy S. Pollard , Siddhartha S. Srinivasa

We consider the problem of interactive partially observable Markov decision processes (I-POMDPs), where the agents are located at the nodes of a communication network. Specifically, we assume a certain message type for all messages.…

Artificial Intelligence · Computer Science 2020-11-11 Yitao Chen , Deepanshu Vasal

Partially Observable Markov Decision Processes (POMDPs) are a natural and general model in reinforcement learning that take into account the agent's uncertainty about its current state. In the literature on POMDPs, it is customary to assume…

Machine Learning · Computer Science 2022-03-24 Noah Golowich , Ankur Moitra , Dhruv Rohatgi

The decoupling of data and control planes, as proposed for 5G networks, will enable the efficient implementation of multitier networks where user equipment (UE) nodes obtain coverage and connectivity through the top-tier macro-cells, and,…

Information Theory · Computer Science 2018-03-22 Ozgun Y. Bursalioglu , Giuseppe Caire , Ratheesh K. Mungara , Haralabos C. Papadopoulos , Chenwei Wang

Partially observable Markov decision processes (POMDPs) provide a modeling framework for a variety of sequential decision making under uncertainty scenarios in artificial intelligence (AI). Since the states are not directly observable in a…

Systems and Control · Computer Science 2019-05-21 Mohamadreza Ahmadi , Nils Jansen , Bo Wu , Ufuk Topcu

Decision making under uncertainty is at the heart of any autonomous system acting with imperfect information. The cost of solving the decision making problem is exponential in the action and observation spaces, thus rendering it unfeasible…

Artificial Intelligence · Computer Science 2024-06-18 Tom Yotam , Vadim Indelman

Recent years have seen human robot collaboration (HRC) quickly emerged as a hot research area at the intersection of control, robotics, and psychology. While most of the existing work in HRC focused on either low-level human-aware motion…

Human-Computer Interaction · Computer Science 2018-04-02 Wei Zheng , Bo Wu , Hai Lin

This paper investigates a cell-free massive multiple-input-multiple-output (CF-mMIMO) integrated sensing and communication (ISAC) system that addresses the critical challenge of information leakage to potential eavesdroppers located within…

Signal Processing · Electrical Eng. & Systems 2026-03-20 Ruiguang Wang , Takumi Takahashi , Hideki Ochiai

Many current large-scale multiagent team implementations can be characterized as following the belief-desire-intention (BDI) paradigm, with explicit representation of team plans. Despite their promise, current BDI team approaches lack tools…

Multiagent Systems · Computer Science 2011-09-13 R. Nair , M. Tambe

We present an alternative view for the study of optimal control of partially observed Markov Decision Processes (POMDPs). We first revisit the traditional (and by now standard) separated-design method of reducing the problem to fully…

Optimization and Control · Mathematics 2024-12-20 Serdar Yüksel

User-centric cell-free (UCCF) massive multiple-input multiple-output (MIMO) systems are considered a viable solution to realize the advantages offered by cell-free (CF) networks, including reduced interference and consistent quality of…

Information Theory · Computer Science 2025-04-15 S. Salehi , S. Mashdour , O. Tamyigit , S. Seyedmasoumian , M. Moradikia , R. C. de Lamare , A. Schmeink

Cell-Free Massive MIMO (CF mMIMO) has emerged as a potential enabler for future networks. It has been shown that these networks are much more energy-efficient than classical cellular systems when they are serving users at peak capacity.…

Signal Processing · Electrical Eng. & Systems 2024-04-30 Robbert Beerten , Vida Ranjbar , Andrea P. Guevara , Hazem Sallouha , Sofie Pollin

In this paper, we consider a queue-aware distributive resource control algorithm for two-hop MIMO cooperative systems. We shall illustrate that relay buffering is an effective way to reduce the intrinsic half-duplex penalty in cooperative…

Machine Learning · Computer Science 2015-05-20 Rui Wang , Vincent K. N. Lau , Ying Cui

We consider partially observable Markov decision processes (POMDPs) with a set of target states and positive integer costs associated with every transition. The traditional optimization objective (stochastic shortest path) asks to minimize…

Artificial Intelligence · Computer Science 2016-05-12 Tomáš Brázdil , Krishnendu Chatterjee , Martin Chmelík , Anchit Gupta , Petr Novotný

We address the problem of controlling a mobile robot to explore a partially known environment. The robot's objective is the maximization of the amount of information collected about the environment. We formulate the problem as a partially…

Robotics · Computer Science 2017-03-08 Mikko Lauri , Risto Ritala