Adjustment for Variable Adherence Under Hierarchical Structure
نویسندگان
چکیده
منابع مشابه
Variable Structure Behavioural Controller for Multi-agent Systems
In previous papers authors have considered agents as inertia-less self driven particles and designed a flocking algorithm. Application of this algorithm to agents with considerable inertial characteristics needs a behavioural controller. The controller uses the local information and helps every agent to imitate the desired behaviour as a member of the flocking frame which covers the main is...
متن کاملVariable Resolution Hierarchical RL
The contribution of this paper is to introduce heuristics, that go beyond safe state abstraction in hierarchical reinforcement learning, to approximate a decomposed value function. Additional improvements in time and space complexity for learning and execution may outweigh achieving less than hierarchically optimal performance and deliver anytime decision making during execution. Heuristics are...
متن کاملHierarchical Variable Ordering for Multiagent Agreement Problems
The Multiagent Agreement Problem (MAP) is a special form of Distributed Constraint Optimization (DCOP) that requires agents to choose values for variables to satisfy not only their own constraints, but also equality constraints with other agents. For solving MAPs, we introduce the AdoptMVA algorithm which is an extension of the existing Adopt algorithm designed to take advantage of the partial ...
متن کاملVariable selection for spatial random field predictors under a Bayesian mixed hierarchical spatial model.
A health outcome can be observed at a spatial location and we wish to relate this to a set of environmental measurements made on a sampling grid. The environmental measurements are covariates in the model but due to the interpolation associated with the grid there is an error inherent in the covariate value used at the outcome location. Since there may be multiple measurements made on different...
متن کاملLatent-Variable Synchronous CFGs for Hierarchical Translation
Data-driven refinement of non-terminal categories has been demonstrated to be a reliable technique for improving monolingual parsing with PCFGs. In this paper, we extend these techniques to learn latent refinements of single-category synchronous grammars, so as to improve translation performance. We compare two estimators for this latent-variable model: one based on EM and the other is a spectr...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Medical Care
سال: 2017
ISSN: 0025-7079
DOI: 10.1097/mlr.0000000000000464