Automated planning has been a continuous field of study since the 1960s, since the notion
of accomplishing a task using an ordered set of actions resonates with almost every known …

Current domain-independent, classical planners require symbolic models of the problem
domain and instance as input, resulting in a knowledge acquisition bottleneck. Meanwhile …

This paper presents FAMA, a novel approach for learning Strips action models from
observations of plan executions that compiles the learning task into a classical planning …

In this paper, we present a novel approach to incrementally learn an Abstract Model of an
unknown environment, and show how an agent can reuse the learned model for tackling the …

In this paper, we describe an approach for learning planning domain models directly from
natural language (NL) descriptions of activity sequences. The modelling problem has been …

This paper presents a novel approach for learning strips action models from examples that
compiles this inductive learning task into a classical planning task. Interestingly, the …

Current domain-independent, classical planners require symbolic models of the problem
domain and instance as input, resulting in a knowledge acquisition bottleneck. Meanwhile …

Autonomous agents embedded in a physical environment need the ability to recognize
objects and their properties from sensory data. Such a perceptual ability is often …

There is increasing awareness in the planning community that depending on complete
models impedes the applicability of planning technology in many real world domains where …

This paper addresses the challenge of automated numeric domain model acquisition from
observations. Many industrial and commercial applications of planning technology rely on …