Interactive Relational Reinforcement Learning of Concept Semantics (Extended Abstract)

Towards Interactive Relational Reinforcement Learning of Concepts

We present a framework for the interactive machine learning of denotational concept semantics in communication between humans and artificial agents. The capability of software agents and robots to learn how to communicate verbally with human users is obviously highly useful in several real-world applications. Whereas the large majority of existing approaches to the machine learning of word sens...

Integrating Experimentation and Guidance in Relational Reinforcement Learning (Extended Abstract)

Multi-Agent Relational Reinforcement Learning Explorations in Multi-State Coordination Tasks

In this paper we report on using a relational state space in multi-agent reinforcement learning. There is growing evidence in the Reinforcement Learning research community that a relational representation of the state space has many benefits over a propositional one. Complex tasks as planning or information retrieval on the web can be represented more naturally in relational form. Yet, this rel...

Active Learning for Teaching a Robot Grounded Relational Symbols

We investigate an interactive teaching scenario, where a human teaches a robot symbols which abstract the geometric properties of objects. There are multiple motivations for this scenario: First, state-of-the-art methods for relational reinforcement learning demonstrate that we can learn and employ strongly generalizing abstract models with great success for goal-directed object manipulation. H...

SmartGantt - An intelligent system for real time rescheduling based on relational reinforcement learning

With the current trend towards cognitive manufacturing systems to deal with unforeseen events and disturbances that constantly demand real-time repair decisions, learning/reasoning skills and interactive capabilities are important functionalities for rescheduling a shop-floor on the fly taking into account several objectives and goal states. In this work, the automatic generation and update thr...