Fear Learning for Flexible Decision Making in RoboCup: A Discussion

In this paper, we address the stagnation of RoboCup com- petitions in the fields of contextual perception, real-time adaptation and flexible decision-making, mainly in regards to the Standard Platform League (SPL). We argue that our Situation-Aware FEar Learning (SAFEL) model has the necessary tools to leverage the SPL competition in these fields of research, by allowing robot players to learn the behaviour profile of the opponent team at runtime. Later, players can use this knowledge to predict when an undesirable outcome is imminent, thus having the chance to act towards preventing it. We discuss specific scenarios where SAFEL’s associative learning could help to increase the positive outcomes of a team during a soccer match by means of contextual adaptation

Extending DTGolog with options

Recently Boutilier et al. (2000) proposed the language DT-GOLOG which combines explicit agent programming with decision theory. The motivation is that a user often has some idea about how to go about solving a particular problem yet a

Repairing decision-theoretic policies using goal-oriented planning

In this paper we address the problem of how decisiontheoretic policies can be repaired. This work is motivated by observations made in robotic soccer where decision-theoretic policies become invalid due to small deviations during execution; and repairing might pay off compared to re-planning from scratch. Our policies are generated with Readylog, a derivative of Golog based on the situation calculus, which combines programming and planning for agents in dynamic domains. When an invalid policy is detected, the world state is transformed into a PDDL description and a state-of-the-art PDDL planner is deployed to calculate the repair plan