We are pleased to announce the “Fourth International Workshop on Intrinsically-Motivated Open-ended Development (IMOL2019)”, which will be held in Frankfurt Institute for Advanced Studies (FIAS) in Frankfurt (Germany), on 1-2-3 July 2019.
Artificial Intelligence (AI) is improving at an impressive pace. Sophisticated robots and powerful algorithms able to perform increasingly complex tasks are being developed every year. They are not only able to perform complex and lengthy tasks, but they can also discover better, or totally new, ways to support human activity and problem-solving. Impressive new technologies, such as self-driving cars, and sophisticated image and speech recognition systems, have achieved significant breakthroughs, and their impact on human activities are expected to exponentially grow over the near future.
Regardless of the actual complexity of their behaviour, the autonomy and versatility of current artificial agents are still limited compared to what biological agents are capable of. This lack of autonomy in present robots and systems prevents them from fully succeeding in interacting with realistic environments where they have to face situations that are unknown at design-time, where learning needs to be multi-task, incremental, and online, or when new goals/tasks have to be discovered and solved autonomously.
Over the last decade, intrinsically motivated learning (sometimes called “curiosity-driven learning”) has been studied by many researchers as an approach to autonomous lifelong learning in machines. Intrinsic motivations are inspired by the human ability to discover how to produce “interesting” effects on the environment driven by self-generated motivational signals not related to specific tasks or instructions. This research aims to develop agents that acquire, under the guidance of intrinsic motivations, repertoires of diverse skills that are likely to become useful later when specific tasks need to be performed.
Advancing our knowledge in this direction is currently at the forefront of AI research. Indeed, the most impressive AI outcomes, especially those based on deep neural networks, mainly involve perception: notwithstanding the initial successes of approaches such as deep reinforcement learning and end-to-end robot control, the capability and autonomy of intelligent artificial agents on the side of action and motivation are still in their infancies. Research on intrinsically motivated open-ended learning now has an unprecedented opportunity to substantially advance the state-of-the-art. Progress can be made in the direction of autonomous formation of goals, development of parameterized skills able to solve multiple tasks and to transfer/generalize knowledge between them, and composition and organization of multiple goals and skills to form hierarchies able to solve more complex problems.
Following two previous workshops, the highly focused “Fourth International Workshop on Intrinsically-Motivated Open-ended Development (IMOL2019)” aims to further explore the promise of intrinsically motivated open-ended lifelong learning. The workshop aims to be a highly interactive event. To this purpose, it will feature high profile keynote presentations and the participation of an audience of about 60 people. It will foster close interaction among the participants by enabling intense peer-to-peer discussions, poster sessions, and collective round tables directed toward specific objectives. It will be followed by a call for papers to be published in an open journal special issue (e.g. a Frontiers in Neurorobotics Research Topic).
The Workshop will occur over three whole days, with the last session of each day dedicated to a plenary discussion targeting open-ended lifelong learning in autonomous agents and robots, for example:
Autonomous robots lifelong learning
Multi-task reinforcement learning
Deep reinforcement learning
Intrinsic motivations
Curriculum learning
Goal self-generation
Multiple task solution and parameterized skills
Neural/probabilistic representations and abstractions
Architectures for open-ended learning
Goal-based skill learning
Knowledge transfer and avoidance of catastrophic forgetting
Compositionality and chunking
Hierarchies of goals and skills
Mitigating risks of real-world deployment of open-ended learning systems
