Dynamically extending planning models using an ontology

In this paper we couple a deterministic planner with an ontology, in order to adapt to new discoveries during plan execution and to reason about the affordances that are available to the planner as the set of known objects is updated. This allows us to extend the planning agent’s functionality during execution. We use as an example planning for persistent autonomous behaviour in underwater vehicles. Planning in this scenario takes place in a symbolic model of the environment, simulating sequences of possible decisions. Ensuring that the simulation remains robust requires careful matching of the model to the real world, including dynamically updating the model from continuous sensing actions. We describe how our system constructs an initial state for planning, using the ontology; how the ontology is also used to determine the results of each action performed by the planner; and finally demonstrate the performance of the system in a simulation, in which two AUVs are required to cooperate in an unknown environment, demonstrating that with additional reasoning the planning system is able to make new efficient choices, taking advantage of the environment in new ways

Plan-Based Policy-Learning for Autonomous Feature Tracking

Mapping and tracking biological ocean features, such as harmful algal blooms, is an important problem in the environmental sciences. The problem exhibits a high degree of uncertainty, because of both the dynamic ocean context and the challenges of sensing. Plan-based policy learning has been shown to be a powerful technique for obtaining robust intelligent behaviour in the face of uncertainty. In this paper we apply this technique in simulation, to the problem of tracking the outer edge of 2D biological features, such as the surfaces of harmful algal blooms. We show that plan-based policy-learning leads to highly accurate tracking in simulation, even in situations where the uncertainty governing the shape of the patch cannot be directly modelled. We present simulation results that give confidence that the approach could work in practice. We are now collaborating with ocean scientists at MBARI to perform physical tests at sea