The aim of this 2nd edition of the workshop (take a look at the 1st edition EGPAI 2016) is to bring to bear on the expertise of a diverse set of researchers to progress in the evaluation of general purpose AI systems. Up to now, most AI systems are tested on specific tasks. However, to be considered truly intelligent, a system should exhibit enough flexibility to be able to learn how to perform a wide variety of tasks, some of which may not be known until after the system is deployed. This workshop will examine formalisations, methodologies and testbenches for evaluating the numerous aspects of this type of general AI systems. More specifically, we are interested in theoretical or experimental research focused on the development of concepts, tools and clear metrics to characterise and measure the intelligence, and other cognitive abilities, of general AI agents. Furthermore, EGPAI2017 will express interest in the IJCAI2017 special theme on AI & Autonomy. Therefore, the workshop will also focus on the evaluation of the abilities of intelligent and autonomous agents, autonomous robots, software agents (such as artificial life agents) and any sort of autonomous systems capable of operating in long-term, real-world scenarios. There will be a panel dealing with this topic.
We are interested in questions such as: Can the various tasks and benchmarks in AI provide a general basis for evaluation and comparison of a broad range of such systems?, Can there be a theory of tasks, or cognitive abilities, that enables a more direct comparison and characterisation of AI systems?, How much does the specificity of an AI agent relate to how fast it can achieve acceptable performance?, How does the structure of a cognitive system relate to how easy or difficult a task - or various classes of tasks - are for it to perform and learn?
We welcome regular papers, short papers, demo papers about benchmarks or tools, and position papers, and encourage discussions over a broad list of topics (not exhaustive):
We are planning to have a demo session which will present real platforms and ways to evaluate AI systems for several tasks in these platforms. The discussion session will include a panel and a more open discussion about the research challenges around the workshop topics, continuation of the workshop, future initiatives, etc.
David Dowe Associate Professor, Clayton School of Information Technology, Monash University. He works primarily in Minimum Message Length (MML) - a unifying tool in machine learning which combines Bayesianism, (algorithmic) information theory and Kolmogorov complexity. Some of the many areas in which he has applied MML include statistical inference (and model selection and point estimation), prediction, machine learning, econometrics (including time series and panel data), proofs of financial market inefficiency, knowledge discovery, data mining, theories of (quantifying) intelligence and new forms of (universal) intelligence test (for biological and non-biological agents), philosophy of science, the problem of induction, bioinformatics, linguistics (evolutionary [tree] models), image analysis, etc.
MORE KEYNOTE TALKS TO BE ANNOUNCED!
Workshop paper submissions
May 5th, 2017
Workshop paper notifications
June 9th, 2017
June 15th, 2017
August 19th-21st, 2017
WE'D LOVE TO HEAR ABOUT YOUR PROJECT / SOFTWARE / WORK.SUBMIT NOW!
|Marco Baroni||Facebook AI Research|
|Jordi Bieger||CADIA, Reykjavik University|
|Angelo Cangelosi||Plymouth University|
|Helgi P. Helgason||Activity Stream|
|Katja Hofmann||Microsoft Research|
|Sean B. Holden||Cambridge University|
|Estevam R. Hruschka||Carnegie Mellon University|
|Armand Joulin||Facebook AI Research|
|Edward Keedwell||Exeter University|
|Tomas Mikolov||Facebook AI Research|
|Frans A. Oliehoek||University of Amsterdam|
|Ricardo B.C. Prudencio||Uni. Fed. de Pernambuco|
|Ute Schmid||Bamberg University|
|Pei Wang||Temple University|