N
Nicholas R. Jennings
Researcher at Imperial College London
Publications - 823
Citations - 65994
Nicholas R. Jennings is an academic researcher from Imperial College London. The author has contributed to research in topics: Multi-agent system & Autonomous agent. The author has an hindex of 116, co-authored 807 publications receiving 64112 citations. Previous affiliations of Nicholas R. Jennings include University of Warsaw & University of Southampton.
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Proceedings ArticleDOI
EA2: The Winning Strategy for the Inaugural Lemonade Stand Game Tournament
TL;DR: This approach is designed to be adaptive to various types of opponents such that coordination is almost always achieved, which yields consistently high utilities to the authors' agent, as evidenced by the Tournament results and the subsequent experimental analysis.
Proceedings ArticleDOI
A heuristic bidding strategy for multiple heterogeneous auctions
TL;DR: A heuristic decision making framework is reported on that enables an autonomous agent to adopt varying tactics and strategies that attempt to ensure that the user's objectives are satisfied when bidding across multiple heterogeneous auctions.
Proceedings Article
Flexible Provisioning of Service Workflows
TL;DR: In this paper, a number of heuristics that vary provisioning according to the predicted performance of provider agents are proposed, leading to a 350% improvement in average utility, while successfully completing 5--6 times as many workflows as current approaches.
U-GDL: A decentralised algorithm for DCOPs with Uncertainty
TL;DR: U-DCOPs with uncertainty is introduced, a novel generalisation of the canonical DCOP framework where the outcomes of local functions are represented by random variables, and the global objective is to maximise the expectation of an arbitrary utility function applied over the sum of these local functions.
Proceedings ArticleDOI
Planning against fictitious players in repeated normal form games
TL;DR: It is shown how an unbounded memory opponent can also be modelled as a finite MDP and presented a new efficient algorithm that can find a way to exploit the opponent by computing in polynomial time a sequence of play that can obtain a higher average reward than those obtained by playing a game theoretic (Nash or correlated) equilibrium.