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BEGIN:VEVENT
DTSTAMP:20261025T173107Z
DTSTART;VALUE=DATE:20270422
DURATION:P1D
SUMMARY:Prob_AI at the Interface of Reinforcement Learning & Evolutionary
  Biology 2027
UID:20270422-8ac672c4a0f6fb1201a101bf7a794ae4@warwick.ac.uk
CREATED:20261003T123257Z
DESCRIPTION:Reinforcement Learning (RL) has emerged as a fundamental tool
  for the design of artificial agents possessing optimality and safety gu
 arantees. A series of results has established that the behaviour learnt 
 by RL agents interacting with one another can be understood using the to
 ols of evolutionary game theory – for example\, one can investigate cond
 itions under which agents’ learnt policies converge to Evolutionarily St
 able Strategies of the underlying game. Issues such as the emergence of 
 cooperation\, the tragedy of the commons\, partner selection\, populatio
 n structure\, and the exploration/exploitation trade-off are key questio
 ns in the study of both artificial intelligence and evolutionary biology
  and can be described using similar underlying mathematical frameworks. 
 The aim of this workshop is to explore recent developments at this inter
 face\, to discuss open questions\, and to bring together researchers fro
 m both communities.
LOCATION:MS0.7\, MSB\, University of ÌÇÐÄTV
CATEGORIES:Workshops,CRiSM Workshops,CRiSM,Conference
LAST-MODIFIED:20261003T123257Z
ORGANIZER;CN=Kathrin Schutrumpf:
END:VEVENT
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