Robots need task planning algorithms to sequence actions toward accomplishing goals that
are impossible through individual actions. Off-the-shelf task planners can be used by …

Autonomous mobile robots employed in industrial applications often operate in complex and
uncertain environments. In this paper we propose an approach based on an extension of …

When machine learning programs from data, we ideally want to learn efficient rather than
inefficient programs. However, existing inductive logic programming (ILP) techniques cannot …

Most logic-based machine learning algorithms rely on an Occamist bias where textual
complexity of hypotheses is minimised. Within Inductive Logic Programming (ILP), this …

The action language BC provides an elegant way of formalizing dynamic domains which
involve indirect effects of actions and recursively defined fluents. In complex robot task …

Reinforcement learning (RL) enables an agent to learn from trial-and-error experiences
toward achieving long-term goals; automated planning aims to compute plans for …

Effective human-robot collaboration (HRC) requires extensive communication among the
human and robot teammates, because their actions can potentially produce conflicts …

A declarative action model is a compact representation of the state transitions of dynamic
systems that generalizes over world objects. The specification of declarative action models …

Non-stationary domains, where unforeseen changes happen, present a challenge for
agents to find an optimal policy for a sequential decision making problem. This work …

A smart home needs to be human-centric, where it tries to fulfill human needs given the
devices it has. Various works are developed to provide homes with reasoning and planning …