DeepAI AI Chat
Log In Sign Up

Plan Development using Local Probabilistic Models

by   Ella M. Atkins, et al.

Approximate models of world state transitions are necessary when building plans for complex systems operating in dynamic environments. External event probabilities can depend on state feature values as well as time spent in that particular state. We assign temporally -dependent probability functions to state transitions. These functions are used to locally compute state probabilities, which are then used to select highly probable goal paths and eliminate improbable states. This probabilistic model has been implemented in the Cooperative Intelligent Real-time Control Architecture (CIRCA), which combines an AI planner with a separate real-time system such that plans are developed, scheduled, and executed with real-time guarantees. We present flight simulation tests that demonstrate how our probabilistic model may improve CIRCA performance.


page 1

page 2

page 3

page 4

page 5

page 6

page 7

page 8


A Novel Algorithm for Real-time Procedural Generation of Building Floor Plans

Real-time generation of natural-looking floor plans is vital in games wi...

Probabilistic Systems with Hidden State and Unobservable Transitions

We consider probabilistic systems with hidden state and unobservable tra...

Funnel Libraries for Real-Time Robust Feedback Motion Planning

We consider the problem of generating motion plans for a robot that are ...

Computational Logic Foundations of KGP Agents

This paper presents the computational logic foundations of a model of ag...

Millions of 5-State n^3 Sequence Generators via Local Mappings

In this paper, we come back on the notion of local simulation allowing t...

Filter-Based Abstractions with Correctness Guarantees for Planning under Uncertainty

We study planning problems for continuous control systems with uncertain...

A Correctness Result for Synthesizing Plans With Loops in Stochastic Domains

Finite-state controllers (FSCs), such as plans with loops, are powerful ...