Please login to be able to save your searches and receive alerts for new content matching your search criteria.
The 2021 edition of AAMAS, the International Conference on Autonomous Agents and Multiagent Systems, took place from the 3rd to 7th of May 2021 (aamas2021.soton.ac.uk). This year it was organized in the form of a virtual event and attracted over 1,000 ...
One of the key challenges for multi-agent learning is scalability. We introduce a technique for speeding up multi-agent learning by exploiting concurrent and incremental experience sharing. This solution adaptively identifies opportunities to transfer ...
Technology for supporting people in their daily lives such as personal assistant agents and smart homes carry great potential for making our lives more connected, healthy, efficient and safe by executing tasks on our behalf and guiding our actions. We ...
Coordinating multi-agent reinforcement learning provides a promising approach to scaling learning in large cooperative multi-agent systems. It allows agents to learn local decision policies based on their local observations and rewards, and, meanwhile, ...
Learning consistent policies in decentralized settings is often problematic. The agents have a myopic view of their neighboring states that could lead to inconsistent action choices. The fundamental question addressed in this work is how to determine ...
Many distributed constraint optimization (DCOP) algorithms include nodes' local maximization operation that searches for the optimal variable assignment in a limited context. When the variable domain is discrete, this operation is exponential in the ...
We present a novel ensemble architecture for learning problem-solving techniques from a very small number of expert solutions and demonstrate its effectiveness in a complex real-world domain. The key feature of our “Generalized Integrated Learning ...
Many message passing algorithms on graphical models include maximization operations on sums of local node function and message values from neighbors. In recent work by McAuly et al, faster maximization computation was achieved in a static environment by ...
Solving a coordination problem in a decentralized environment requires a large amount of resources and thus exploiting the innate system structure and external information as much as possible is necessary for such a problem to be solved in a ...
This work proposes new techniques for saving communication and computational resources when solving distributed constraint optimization problems in an environment where system hardware resources are clustered. Using a pre-computed policy and two phase ...
Distributed collaborative adaptive sensing (DCAS) of the atmosphere is a new paradigm for detecting and predicting hazardous weather using a large dense network of short-range, low-powered radars to sense the lowest few kilometers of the earths ...
It is crucial for embedded systems to adapt to the dynamics of open environments. This adaptation process becomes especially challenging in the context of multiagent systems. In this paper, we argue that multiagent meta-level control is an effective way ...
The problem of finding agents' rational strategies in bargaining with incomplete information is well known to be challenging. The literature provides a collection of results for very narrow uncertainty settings, but no generally applicable algorithm. In ...
We consider the problem of allocating networked resources in dynamic environment, such as cloud computing platforms, where providers strategically price resources to maximize their utility. Resource allocation in these environments, where both providers ...
Decentralized reinforcement learning (DRL) has been applied to a number of distributed applications. However, one of the main challenges faced by DRL is its convergence. Previous work has shown that hierarchically organizational control is an effective ...
It is a challenging problem to find agents’ rational strategies in bargaining with incomplete information. In this paper we perform a game theoretic analysis of agents’ rational strategies in finite horizon bilateral bargaining with one-sided ...
In building practical sensor networks, it is often beneficial to use only a subset of sensors to take measurements because of computational, communication, and power limitations. Thus, selecting a subset of nodes to perform measurements whose results ...
Variants of the decentralized MDP model focus on problems exhibiting some special structure that makes them easier to solve in practice. Our work is concerned with two main issues. First, we propose a new model, Event-Driven Interaction with Complex ...
In situations where Bayesian networks (BN) inferencing approximation is allowable, we show how to reduce the amount of sensory observations necessary and in a multi-agent context the amount of agent communication. To achieve this, we introduce Pseudo-...
Several multiagent reinforcement learning (MARL) algorithms have been proposed to optimize agents' decisions. Due to the complexity of the problem, the majority of the previously developed MARL algorithms assumed agents either had some knowledge of the ...