Bayesian Execution Skill Estimation

Task Execution: Interfacing to Reactive Skill Networks

We describe an implemented two layer architecture for real-time task execution in physical agents. The system integrates the RAP system with a set of dynamically configurable reactive processes called skills. Thus the notion of a primitive action is replaced by the selection of a set of skills that interact directly with each other and with the world to form a reactive agent. Using this approac...

Score-Based Bayesian Skill Learning

We extend the Bayesian skill rating system of TrueSkill to accommodate score-based match outcomes. TrueSkill has proven to be a very effective algorithm for matchmaking — the process of pairing competitors based on similar skill-level — in competitive online gaming. However, for the case of two teams/players, TrueSkill only learns from win, lose, or draw outcomes and cannot use additional match...

TrueSkillTM: A Bayesian Skill Rating System

The temporal dynamics of strategy execution in cognitive skill learning

The transition from algorithmic to memory-based performance is a core component of cognitive skill learning. There has been debate about the temporal dynamics of strategy execution, with some models assuming a race (i.e., independent, capacity unconstrained parallel processing) between algorithm and retrieval, and others assuming a choice mechanism. The authors investigated this issue using a n...

Execution Monitoring with Quantitative Temporal Bayesian Networks

The goal of execution monitoring is to determine whether a system or person is following a plan appropriately. Monitoring information may be uncertain, and the plan being monitored may have complex temporal constraints. We develop a new framework for reasoning under uncertainty with quantitative temporal constraints – Quantitative Temporal Bayesian Networks – and we discuss its application to p...