The development of autonomous agents that perform tasks with the same dexterity as
performed by humans is one of the challenges of artificial intelligence and robotics.
This motivates the research on intelligent agents, since the agent must choose the best
action in a dynamic environment in order to maximise the final score. In this context,
the present paper introduces a novel algorithm for Qualitative Case-Based Reasoning and
Learning (QCBRL), which is a case-based reasoning system that uses qualitative spatial
representations to retrieve and reuse cases by means of relations between objects in the
environment. Combined with reinforcement learning, QCBRL allows the agent to learn
new qualitative cases at runtime, without assuming a pre-processing step. In order to
avoid cases that do not lead to the maximum performance, QCBRL executes case-base
maintenance, excluding these cases and obtaining new (more suitable) ones. Experimental
evaluation of QCBRL was conducted in a simulated robot-soccer environment, in a real
humanoid-robot environment and on simple tasks in two distinct gridworld domains.
Results show that QCBRL outperforms traditional RL methods. As a result of running QCBRL
in autonomous soccer matches, the robots performed a higher average number of goals
than those obtained when using pure numerical models. In the gridworlds considered, the
agent was able to learn optimal and safety policies.