The 2nd international workshop on evaluating general-purpos AI (EGPAI2017) will be held in conjunction with IJCAI 2017 in Melbourne, Australia (August 19-21, 2017). Take a look at the 1st edition EGPAI 2016)

Up to now, most AI systems are tested on specific tasks. However, to be considered truly intelligent, a system should exhibit enough flexibility to find a diversity of solutions for a range of tasks, some of which may not be known until after the system is deployed. Very recently there has been a large number of events, challenges and platforms that are giving a new perspective to how AI can be evaluated, such as the Arcade Learning Environment video games, the Video Game Definition Language (VGDL), OpenAI Gym, Microsoft Malmo, OpenAI Universe, Facebook TorchCarft, Facebook CommAI-env, GoodAI school, Google DeepMind Lab, etc. This workshop will welcome 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 participate in the IJCAI2017 special theme on AI & Autonomy. Therefore, the workshop will welcome papers on the evaluation of autonomous agents of any kind, such as robots, software agents, 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):

  • Analysis and comparisons of AI benchmarks and competitions. Lessons learnt.
  • Proposals for new general tasks, evaluation environments, workbenches and general AI development platforms.
  • Theoretical or experimental accounts of the space of tasks, abilities and their dependencies.
  • Evaluation of development in robotics and other autonomous agents, and cumulative learning in general learning systems.
  • Tasks and methods for evaluating: transfer learning, cognitive growth, structural self-modification and self-programming.
  • Evaluation of social, verbal and other general abilities in multi-agent systems, video games and artificial social ecosystems.
  • Evaluation of autonomous systems: cognitive architectures and multi-agent systems versus general components: machine learning techniques, SAT solvers, planners, etc.
  • Unified theories for evaluating intelligence and other cognitive abilities, independently of the kind of subject (humans, animals or machines): universal psychometrics.
  • Analysis of reward aggregation and utility functions, environment properties (Markov, ergodic, etc.) in the characterisation of reinforcement learning tasks.
  • Methods supporting automatic generation of tasks and problems with systematically introduced variations.
  • Better understanding of the characterisation of task requirements and difficulty (energy, time, trials needed..), beyond algorithmic complexity.
  • Evaluation of AI systems using generalised cognitive tests for humans. Computer models taking IQ tests. Psychometric AI.
  • Adaptation of evaluation tools from comparative psychology and psychometrics to AI: Item Response Theory (IRT), adaptive testing, hierarchical factor analysis.
  • Evaluation methods for multiresolutional perception in AI systems and agents.

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.



Workshop paper submissions

May 5th, 2017

Workshop paper notifications

June 9th, 2017

Final submission

June 15th, 2017

Workshop date

August 19th-21st, 2017

All deadlines are at 11:59PM UTC-12.


  • We solicit submissions (full or short papers) including: original research contributions, applications and experiences, surveys, comparisons, and state-of-the-art reports, tool or demo papers, position papers related to the topics mentioned above and work in progress papers.
  • Submitted papers must be formatted according to the camera-ready style for IJCAI 2017, and submitted electronically in PDF format through Easychair
  • Papers are allowed a maximum six (6) pages, excluded references. References can take up to one page. Formatting Guidelines, LaTeX Styles and Word Template can be downloaded from here.
  • Authorship is not anonymous (single-blind review). Papers will be reviewed by the program committee.
  • The designated author will be notified by email about acceptance or rejection by TBA. Details of the reviewing process will be posted on the EGPAI 2017 website.



Presentation and publication

  • Authors of accepted papers will be asked to present the paper during the workshop.
  • Online pre-proceedings containing all accepted papers will be prepared before the date of the conference.
  • Depending on the number and quality of submissions, we will examine the possibility of targeting a volume or a journal special issue.


Name Affiliation
Marco Baroni Facebook AI Research
Jordi Bieger CADIA, Reykjavik University
Angelo Cangelosi Plymouth University
Emmanuel Dupoux EHESS
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
Jan Koutnik IDSIA
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
Bas Steunebrink IDSIA
Pei Wang Temple University



Technical University of Valencia


Chalmers University of Technology


Reykjavik University and the Icelandic Institute for Intelligent Machines


Technical University of Valencia.

(Contact person)

Monash University