Efficient Symbolic Reasoning for First-Order MDPs
نویسندگان
چکیده
We propose an algorithm, referred to as ALLTHETA, for performing efficient domain-independent symbolic reasoning in a planning system FLUCAP 1.1 that solves first-order MDPs. The computation is done avoiding vicious state and action grounding.
منابع مشابه
Say “No” to Grounding: An Inference Algorithm for First-Order MDPs
We propose an algorithm, referred to as ALLTHETA, for performing efficient domain-independent symbolic reasoning in a planning system FLUCAP that solves first-order MDPs. The computation is done avoiding vicious grounding.
متن کاملSolving Relational MDPs with Exogenous Events and Additive Rewards
We formalize a simple but natural subclass of service domains for relational planning problems with object-centered, independent exogenous events and additive rewards capturing, for example, problems in inventory control. Focusing on this subclass, we present a new symbolic planning algorithm which is the first algorithm that has explicit performance guarantees for relational MDPs with exogenou...
متن کاملSemi-Symbolic Computation of Efficient Controllers in Probabilistic Environments
We present a semi-symbolic algorithm for synthesizing efficient controllers in a stochastic environment, implemented as an add-on to the probabilistic model checker PRISM. The user specifies the environment and the controllable actions using a Markov Decision Process (MDP), modeled in the PRISM language. Controller efficiency is defined with respect to a user-specified assignment of costs and r...
متن کاملLow-Dimensional Embeddings of Logic
Many machine reading approaches, from shallow information extraction to deep semantic parsing, map natural language to symbolic representations of meaning. Representations such as first-order logic capture the richness of natural language and support complex reasoning, but often fail in practice due to their reliance on logical background knowledge and the difficulty of scaling up inference. In...
متن کاملRelational Knowledge Extraction from Neural Networks
The effective integration of learning and reasoning is a well-known and challenging area of research within artificial intelligence. Neural-symbolic systems seek to integrate learning and reasoning by combining neural networks and symbolic knowledge representation. In this paper, a novel methodology is proposed for the extraction of relational knowledge from neural networks which are trainable ...
متن کامل