Using Kernel Perceptrons to Learn Action Effects for Planning

Abstract — We investigate the problem of learning action effects in STRIPS and ADL planning domains. Our approach is based on a kernel perceptron learning model, where action and state information is encoded in a compact vector representation as input to the learning mechanism, and resulting state changes are produced as output. Empirical results of our approach indicate efficient training and prediction times, with low average error rates (< 3%) when tested on STRIPS and ADL versions of an object manipulation scenario. This work is part of a project to integrate machine learning techniques with a planning system, as part of a larger cognitive architecture linking a highlevel reasoning component with a low-level robot/vision system. I

Object Action Complexes as an Interface for Planning and Robot Control

Abstract — Much prior work in integrating high-level artificial intelligence planning technology with low-level robotic control has foundered on the significant representational differences between these two areas of research. We discuss a proposed solution to this representational discontinuity in the form of object-action complexes (OACs). The pairing of actions and objects in a single interface representation captures the needs of both reasoning levels, and will enable machine learning of high-level action representations from low-level control representations. I. Introduction and Background The different representations that are effective for continuous control of robotic systems and the discrete symbolic AI presents a significant challenge for integrating AI planning research and robotics. These areas of research should be abl

Learning action representations using kernel perceptrons

Action representation is fundamental to many aspects of cognition, including language. Theories of situated cognition suggest that the form of such representation is distinctively determined by grounding in the real world. This thesis tackles the question of how to ground action representations, and proposes an approach for learning action models in noisy, partially observable domains, using deictic representations and kernel perceptrons. Agents operating in real-world settings often require domain models to support planning and decision-making. To operate effectively in the world, an agent must be able to accurately predict when its actions will be successful, and what the effects of its actions will be. Only when a reliable action model is acquired can the agent usefully combine sequences of actions into plans, in order to achieve wider goals. However, learning the dynamics of a domain can be a challenging problem: agents’ observations may be noisy, or incomplete; actions may be non-deterministic; the world itself may be noisy; or the world may contain many objects and relations which are irrelevant. In this thesis, I first show that voted perceptrons, equipped with the DNF family of kernels, easily learn action models in STRIPS domains, even when subject to noise and partial observability. Key to the learning process is, firstly, the implicit exploration of the space of conjunctions of possible fluents (the space of potential action preconditions) enabled by the DNF kernels; secondly, the identification of objects playing similar roles in different states, enabled by a simple deictic representation; and lastly, the use of an attribute-value representation for world states. Next, I extend the model to more complex domains by generalising both the kernel and the deictic representation to a relational setting, where world states are represented as graphs. Finally, I propose a method to extract STRIPS-like rules from the learnt models. I give preliminary results for STRIPS domains and discuss how the method can be extended to more complex domains. As such, the model is both appropriate for learning data generated by robot explorations as well as suitable for use by automated planning systems. This combination is essential for the development of autonomous agents which can learn action models from their environment and use them to generate successful plans

Learning STRIPS Operators from Noisy and Incomplete Observations

Agents learning to act autonomously in real-world domains must acquire a model of the dynamics of the domain in which they operate. Learning domain dynamics can be challenging, especially where an agent only has partial access to the world state, and/or noisy external sensors. Even in standard STRIPS domains, existing approaches cannot learn from noisy, incomplete observations typical of real-world domains. We propose a method which learns STRIPS action models in such domains, by decomposing the problem into first learning a transition function between states in the form of a set of classifiers, and then deriving explicit STRIPS rules from the classifiers' parameters. We evaluate our approach on simulated standard planning domains from the International Planning Competition, and show that it learns useful domain descriptions from noisy, incomplete observations.Comment: Appears in Proceedings of the Twenty-Eighth Conference on Uncertainty in Artificial Intelligence (UAI2012