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Multiagent Systems: Challenges and Opportunities for Decision-Theoretic Planning
1999
The AI Magazine
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In this article, I describe several challenges facing the integration of two distinct lines of AI research: (1) decision-theoretic planning (DTP) and (2) multiagent systems. ...
By integrating models of DTP in multiagent systems research, more sophisticated multiagent planning scenarios can be accommodated, at the same time explaining precisely how agents determine their valuations ...
In sequential decision making under uncertainty, say, that involves the solution of an MDP, an agent generally considers a number of potential courses of action and settles on the one with the highest ...
doi:10.1609/aimag.v20i4.1477
dblp:journals/aim/Boutilier99
fatcat:6ydnkod7bjh7zgiw2bwapxmacy
Multiagent Decision Making and Learning in Urban Environments
2019
Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence
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In this paper, I will overview some of our recent contributions towards developing planning and reinforcement learning strategies to address several such challenges present in large-scale urban multiagent ...
Achieving coordination among agents in such urban settings presents several algorithmic challenges---ability to scale to thousands of agents, addressing uncertainty, and partial observability in the environment ...
Decentralized partially observable MDP (Dec-POMDPs) have emerged as a popular framework for modeling such multiagent sequential decision making problems under uncertainty [Bernstein et al., 2002; Kumar ...
doi:10.24963/ijcai.2019/895
dblp:conf/ijcai/Kumar19
fatcat:m7pe5nx2pfehndnmbyvk2hyete
Optimizing Consensus-based Multi-target Tracking with Multiagent Rollout Control Policies
[article]
2021
arXiv
pre-print
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multiagent distributed decision technique. ...
This paper considers a multiagent, connected, robotic fleet where the primary functionality of the agents is sensing. ...
A multiagent version of rollout is introduced recently by Bertsekas in [13] which maintains a policy improvement property under a sequential decision making with respect to agents. ...
arXiv:2102.02919v1
fatcat:xzsa3fwuzzdu5pe33rjhbllgem
Decision Making in Complex Multiagent Contexts: A Tale of Two Frameworks
2012
The AI Magazine
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In this article, I focus on decision making in a multiagent context with partial information about the problem. ...
Decision making is a key feature of autonomous systems. ...
Despite the inherent complexity, decision-theoretic frameworks such as POMDPs offer a principled and theoretically sound formalism for decision making under uncertainty with guarantees of optimality of ...
doi:10.1609/aimag.v33i4.2402
fatcat:peqlr3rr5bghffao6zjowl7amy
Conflicts in teamwork
2005
Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems - AAMAS '05
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Today within the AAMAS community, we see at least four competing approaches to building multiagent systems: beliefdesire-intention (BDI), distributed constraint optimization (DCOP), distributed POMDPs, ...
This paper highlights hybrid approaches for multiagent teamwork. In particular, for the past decade, the TEAMCORE research group has focused on building agent teams in complex, dynamic domains. ...
Though MDPs provide for sequential decision making in the presence of transitional uncertainty, they are hampered in not being able to handle observational uncertainty. ...
doi:10.1145/1082473.1082474
dblp:conf/atal/TambeBJKMMMNOPPPSSV05
fatcat:kjvy6vfd2jd3xo4kopie5irlbu
A Decentralized Partially Observable Markov Decision Model with Action Duration for Goal Recognition in Real Time Strategy Games
2017
Discrete Dynamics in Nature and Society
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with resampling for inference under the dynamic Bayesian network structure of Dec-POMDM-T. ...
Multiagent goal recognition is a tough yet important problem in many real time strategy games or simulation systems. ...
Acknowledgments This work is sponsored by the National Natural Science Foundation of China under Grant no. 61473300. ...
doi:10.1155/2017/4580206
fatcat:rvo36c37jfbqhngux45uawdtcy
Dynamic Incentive Mechanisms
2010
The AI Magazine
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The framework of dynamic mechanism design embraces coordinated decision-making both in the context of uncertainty about the world external to an agent and also in regard to the dynamics of agent preferences ...
This is a natural development, as AI is increasingly used for automated decision making in real-world settings. ...
for sequential decision making with agents that face internal uncertainty. ...
doi:10.1609/aimag.v31i4.2316
fatcat:vgpmc3dn2zb6lgr33wuml3guge
A Bayesian game based adaptive fuzzy controller for multiagent POMDPs
2010
International Conference on Fuzzy Systems
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Multiagent POMDPs have emerged as a powerful framework for modeling and optimizing multiagent sequential decision making problems under uncertainty, but finding optimal policies is computationally very ...
This paper develops a novel fuzzy reinforcement learning (RL) based controller for multiagent partially observable Markov decision processes (POMDPs) modeled as a sequence of Bayesian games. ...
INTRODUCTION Optimization of sequential decision making problems under uncertainty has been an active area of research for over three decades now spanning diverse fields such as Artificial Intelligence ...
doi:10.1109/fuzzy.2010.5584614
dblp:conf/fuzzIEEE/SharmaS10
fatcat:6pi5lklsk5dblefimieahyx6ma
Decentralized control of partially observable Markov decision processes
2013
52nd IEEE Conference on Decision and Control
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Markov decision processes (MDPs) are often used to model sequential decision problems involving uncertainty under the assumption of centralized control. ...
with different assumptions about uncertainty and agent independence. ...
INTRODUCTION Optimal sequential decision making and control problems under uncertainty have been extensively studied both in the artificial intelligence and control systems literature (see e.g. [1]- [ ...
doi:10.1109/cdc.2013.6760239
dblp:conf/cdc/AmatoCGUK13
fatcat:fe5yksf4zjfnrmhswb2rspipji
Constrained Multiagent Markov Decision Processes: a Taxonomy of Problems and Algorithms
2021
The Journal of Artificial Intelligence Research
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Although several models and algorithms for such constrained multiagent planning problems under uncertainty have been proposed in the literature, it remains unclear when which algorithm can be applied. ...
In this survey we conceptualize these domains and establish a generic problem class based on Markov decision processes. ...
This work is funded by distribution system operator Alliander, and by the Netherlands Organisation for Scientific Research (NWO), as part of the Uncertainty Reduction in Smart Energy Systems program. ...
doi:10.1613/jair.1.12233
fatcat:dxezhz6avvbj3jahq3tce75i3e
Optimal Decision-Making in Mixed-Agent Partially Observable Stochastic Environments via Reinforcement Learning
[article]
2019
arXiv
pre-print
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Optimal decision making with limited or no information in stochastic environments where multiple agents interact is a challenging topic in the realm of artificial intelligence. ...
the theory and algorithm to the multiagent setting. ...
Models of Decision Making The task of making decisions in a sequential, stochastic environment can be modeled with the Markov decision process (MDP) framework [5] , which describes the features of the ...
arXiv:1901.01325v1
fatcat:36i4dwlgfngzfi4bzlvfpnurze
A Multiagent Planning Approach for Cooperative Patrolling with Non-Stationary Adversaries
2017
International journal on artificial intelligence tools
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Multiagent patrolling is the problem faced by a set of agents that have to visit a set of sites to prevent or detect some threats or illegal actions. ...
In this paper, we propose a multiagent planning approach that enables effective cooperation between a team of patrollers in uncertain environments. ...
for formalizing cooperative distributed decision-making problems under uncertainty. ...
doi:10.1142/s0218213017600181
fatcat:he7ezbo2yngnxj4qczo4b4et4m
Leveled-Commitment Contracting: A Backtracking Instrument for Multiagent Systems
2002
The AI Magazine
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Multiagent Negotiation under Time
have already been taken (Larson and
other negotiations or the environment. Constraints. ...
A recent the sequential mechanisms, but mul-
surplus might only be maximized paper shows that if the contract is opti- tiple equilibria can exist in the simul-
under certain splits of the ...
doi:10.1609/aimag.v23i3.1659
dblp:journals/aim/SandholmL02
fatcat:hzj3s2jyw5dppnmgmyktv3x2bu
Preference Handling in Combinatorial Domains: From AI to Social Choice
2008
The AI Magazine
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In both individual and collective decision making, the space of alternatives from which the agent (or the group of agents) has to choose often has a combinatorial (or multi-attribute) structure. ...
We give an introduction to preference handling in combinatorial domains in the context of collective decision making, and show that the considerable body of work on preference representation and elicitation ...
The work of the second author has been partially supported by the Dutch NWO Vidi project "Collective Decision Making in Combinatorial Domains". ...
doi:10.1609/aimag.v29i4.2201
fatcat:csbkqo4bx5ec5ntcmaywnrzzzy
Multiagent Decision Making in Collaborative Decision Networks by Utility Cluster Based Partial Evaluation
2015
International Journal of Uncertainty Fuzziness and Knowledge-Based Systems
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We consider optimal multiagent cooperative decision making in stochastic environments. The focus is on simultaneous decision making, during which agents cooperate by limited communication. ...
We model the multiagent system as a collaborative decision network (CDN). Several techniques are developed to improve efficiency for decision making with CDNs. ...
For sequential decision making, IDs allow chance parents for decision nodes, signifying observations available prior to making each decision. ...
doi:10.1142/s0218488515500075
fatcat:zfhjq3q7hbf3faadsltgrky6ee
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