Computers are not just better than us at memory or calculation. They are better than us at a poker table.
In December 2017, Carnegie Mellon poker playing computer Libratus has stunned the world by winning 1.7M in a 20 days tournament against four poker stars.
In a nutshell, Libratus is a decision-making agent that takes decisions in an uncertain environment, exploring the potential consequences of their own choices using complex estimates of the world around.
This course is a study of the basic building blocks of decision-making agents, which are abstract entities living in an uncertain environment and are guided towards the realisation of given objectives.
An agent is typically endowed with a knowledge base, a collection of facts expressed in some logical language, and an action repertoire at each state. The agent can reason about the environment, using their knowledge base, and take decisions accordingly. The environment is typically unknown, stochastic, and evolves following some rules that might be unknown to the agent, as well. On top of this, it is usually inhabited by other agents, which may or may not strive to achieve similar objectives. The task is to take the best possible decision that can be taken given the (incomplete) information available.
This simple model is the basis of a number of important achievements in AI, and combines the use of logical, game-theoretic and algorithmic analysis.
Dr Paolo Turrini, Associate Professor, Department of Computer Science, University of Warwick
There are no prerequisites for this course. This course is open to students studying any discipline at University level. We welcome individuals from all backgrounds, including students who are currently studying another subject but who want to broaden their knowledge in another discipline. Students should also meet our standard entry requirements and must be aged 18 or over by the time the Summer School commences and have a good understanding of the English language.
The course will be an investigation of the most important developments of AI in multi-agent contexts, touching upon themes such as opponent modelling, games with imperfect information, resource allocation, collective decision-making and electronic commerce applications.
Credits info: 7.5 EC
You must check with the relevant office of your institution if you will be awarded credit, but many institutions will allow this. In general, you’ll earn 3 credits in the US system, and 7.5 ECTS in the European system. Warwick will provide any necessary supporting evidence to help evaluate the worth of the course.
GBP 2070: Tuition fee (includes a 10% early booking discount, social programme and guest lecture series)
For further information, please click the "LINK TO ORIGINAL" button below.