Learning Features and Abstract Actions for Computing Generalized Plans

Generalized planning is concerned with the computation of plans that solve not one but multiple instances of a planning domain. Recently, it has been shown that generalized plans can be expressed as mappings of feature values into actions, and that they can often be computed with fully observable non-deterministic (FOND) planners. The actions in such plans, however, are not the actions in the instances themselves, which are not necessarily common to other instances, but abstract actions that are defined on a set of common features. The formulation assumes that the features and the abstract actions are given. In this work, we address this limitation by showing how to learn them automatically. The resulting account of generalized planning combines learning and planning in a novel way: a learner, based on a Max SAT formulation, yields the features and abstract actions from sampled state transitions, and a FOND planner uses this information, suitably transformed, to produce the general plans. Correctness guarantees are given and experimental results on several domains are reported.Comment: Preprint of paper accepted at AAAI'19 conferenc

Linguistics Landscape: a Cross Culture Perspective

This paper was to aim in discussing the linguistic landscape. It was the visibility and salience of languages on public and commercial signs in a given territory or region (Landry and Bourhis 1997). The linguistic landscape has been described as being somewhere at the junction of sociolinguistics, sociology, social psychology, geography, and media studies. It is a concept used in sociolinguistics as scholars study how languages are visually used in multilingual societies, from large metropolitan centers to Amazonia. For example, some public signs in Jerusalem are in Hebrew, English, and Arabic (Spolsky and Cooper 1991, Ben-Rafael et al., 2006). Studies of the linguistic landscape have been published from research done around the world. The field of study is relatively recent; the linguistic landscape paradigm has evolved rapidly and while it has some key names associated with it, it currently has no clear orthodoxy or theoretical core

Generalized Potential Heuristics for Classical Planning

Generalized planning aims at computing solutions that work for all instances of the same domain. In this paper, we show that several interesting planning domains possess compact generalized heuristics that can guide a greedy search in guaranteed polynomial time to the goal, and which work for any instance of the domain . These heuristics are weighted sums of state features that capture the number of objects satisfying a certain first-order logic property in any given state. These features have a meaningful interpretation and generalize naturally to the whole domain. Additionally, we present an approach based on mixed integer linear programming to compute such heuristics automatically from the observation of small training instances. We develop two variations of the approach that progressively refine the heuristic as new states are encountered. We illustrate the approach empirically on a number of standard domains, where we show that the generated heuristics will correctly generalize to all possible instances

Evolutionary Algorithms for Reinforcement Learning

There are two distinct approaches to solving reinforcement learning problems, namely, searching in value function space and searching in policy space. Temporal difference methods and evolutionary algorithms are well-known examples of these approaches. Kaelbling, Littman and Moore recently provided an informative survey of temporal difference methods. This article focuses on the application of evolutionary algorithms to the reinforcement learning problem, emphasizing alternative policy representations, credit assignment methods, and problem-specific genetic operators. Strengths and weaknesses of the evolutionary approach to reinforcement learning are presented, along with a survey of representative applications