Learning Symbolic Models of Stochastic Domains

In this article, we work towards the goal of developing agents that can learn to act in complex worlds. We develop a probabilistic, relational planning rule representation that compactly models noisy, nondeterministic action effects, and show how such rules can be effectively learned. Through experiments in simple planning domains and a 3D simulated blocks world with realistic physics, we demonstrate that this learning algorithm allows agents to effectively model world dynamics

Learning probabilistic relational planning rules

To learn to behave in highly complex domains, agents must represent and learn compact models of the world dynamics. In this paper, we present an algorithm for learning probabilistic STRIPS-like planning operators from examples. We demonstrate the effective learning of rule-based operators for a wide range of traditional planning domains

Beyond awareness: the persuasion stage of decision-making explains urban residents’ compliance with landscape irrigation restrictions

Inadequate compliance with landscape irrigation restrictions poses a significant challenge for policymakers, environmental educators, and communicators working in urban water conservation as these policies are only effective if compliance is achieved. This study examined perceptions among Florida, USA residents to whom these policies apply using the Diffusion of Innovations Theory. Among the approximately half of these residents who were aware of these restrictions and had an opportunity to develop perceptions about them, perceptions were moderately positive with compatibility, observability, relative advantage, trialability, and complexity falling between 0.0 and 1.0 on a scale ranging from −2 to + 2. Hierarchical linear regression revealed complexity, compatibility, and relative advantage were significant predictors of intention to comply. Perceived complexity was found to be the biggest barrier to individuals’ intent to comply. Policymakers and others working in urban water conservation need to simplify both irrigation restrictions and education about them