159 citations · 218 across the 12 of their papers we have counts for
5 papers · 1 filter
A Data-Driven Method for Recognizing Automated Negotiation Strategies
Ming Li, Pradeep K. Murukannaiah, Catholijn M. Jonker
Understanding an opponent agent helps in negotiating with it. Existing works on understanding opponents focus on preference modeling (or estimating the opponent's utility function)…
Synthesising Reinforcement Learning Policies through Set-Valued Inductive Rule Learning
Youri Coppens, Denis Steckelmacher, Catholijn M. Jonker +1
Today's advanced Reinforcement Learning algorithms produce black-box policies, that are often difficult to interpret and trust for a person. We introduce a policy distilling algori…
More Similar Values, More Trust? -- the Effect of Value Similarity on Trust in Human-Agent Interaction
Siddharth Mehrotra, Catholijn M. Jonker, Myrthe L. Tielman
As AI systems are increasingly involved in decision making, it also becomes important that they elicit appropriate levels of trust from their users. To achieve this, it is first im…
The Second Type of Uncertainty in Monte Carlo Tree Search
Thomas M Moerland, Joost Broekens, Aske Plaat +1
Monte Carlo Tree Search (MCTS) efficiently balances exploration and exploitation in tree search based on count-derived uncertainty. However, these local visit counts ignore a secon…
Think Too Fast Nor Too Slow: The Computational Trade-off Between Planning And Reinforcement Learning
Thomas M. Moerland, Anna Deichler, Simone Baldi +2
Planning and reinforcement learning are two key approaches to sequential decision making. Multi-step approximate real-time dynamic programming, a recently successful algorithm clas…