Guiding Inference Through Relational Reinforcement Learning
نویسندگان
چکیده
Reasoning plays a central role in intelligent systems that operate in complex situations that involve time constraints. In this paper, we present the Adaptive Logic Interpreter, a reasoning system that acquires a controlled inference strategy adapted to the scenario at hand, using a variation on relational reinforcement learning. Employing this inference mechanism in a reactive agent architecture lets the agent focus its reasoning on the most rewarding parts of its knowledge base and hence perform better under time and computational resource constraints. We present experiments that demonstrate the benefits of this approach to reasoning in reactive agents, then discuss related work and directions for future research.
منابع مشابه
MAP inference in Large Factor Graphs with Reinforcement Learning
Large, relational factor graphs with structure defined by first-order logic or other languages give rise to notoriously difficult inference problems. Because unrolling the structure necessary to represent distributions over all hypotheses has exponential blow-up, solutions are often derived from MCMC. However, because of limitations in the design and parameterization of the jump function, these...
متن کاملCan I Do That? Discovering Domain Axioms Using Declarative Programming and Relational Reinforcement Learning
Robots deployed to assist humans in complex, dynamic domains need the ability to represent, reason with, and learn from, different descriptions of incomplete domain knowledge and uncertainty. This paper presents an architecture that integrates declarative programming and relational reinforcement learning to support cumulative and interactive discovery of previously unknown axioms governing doma...
متن کاملGuiding Inference with Policy Search Reinforcement Learning
Symbolic reasoning is a well understood and effective approach to handling reasoning over formally represented knowledge; however, simple symbolic inference systems necessarily slow as complexity and ground facts grow. As automated approaches to ontology-building become more prevalent and sophisticated, knowledge base systems become larger and more complex, necessitating techniques for faster i...
متن کاملRelational Reinforcement Learning
Reinforcement learning [10] is a subtopic of machine learning that is concerned with software systems that learn to behave through interaction with their environment and receive only feedback on the quality of their current behavior instead of a set of correctly labelled learning examples. Although reinforcement learning algorithms have been studied extensively in a propositional setting, their...
متن کاملPolicy Search Based Relational Reinforcement Learning using the Cross - Entropy Method
Relational Reinforcement Learning (RRL) is a subfield of machine learning in which a learning agent seeks to maximise a numerical reward within an environment, represented as collections of objects and relations, by performing actions that interact with the environment. The relational representation allows more dynamic environment states than an attribute-based representation of reinforcement l...
متن کامل