Much research in artificial intelligence is concerned with the development of autonomous
agents that can interact effectively with other agents. An important aspect of such agents is …

The key challenge in multiagent learning is learning a best response to the behaviour of
other agents, which may be non-stationary: if the other agents adapt their strategy as well …

Decision-making complexity, in a distributed environment, is due to hard tasks that a system
must resolve. This complexity makes researchers focus on looking for solutions to cope with …

Opponent modeling is the ability to use prior knowledge and observations in order to predict
the behavior of an opponent. This survey presents a comprehensive overview of existing …

Future AI applications require performance, reliability and privacy that the existing, cloud-
dependant system architectures cannot provide. In this article, we study orchestration in the …

Designing intelligent agents that interact proficiently with people necessitates the prediction
of human decision-making. We present and discuss three prediction paradigms for …

This study proposed a novel reward-based negotiating agent strategy using an issue-based
represented deep policy network. We compared the negotiation strategies with …

The growing capabilities and increasingly widespread deployment of AI systems necessitate
robust benchmarks for measuring their cooperative capabilities. Unfortunately, most multi …

Negotiation is a complex process. The decision making involved in several stages of
negotiation makes its automation complex. In this paper we present a lifecycle model of a …

Despite abundant negotiation strategies in literature, the complexity of automated
negotiation forbids a single strategy from being dominant against all others in different …