UMD     This Site

Integrating acting and planning is a long-standing problem in aritificial intelligence (AI). Despite progress beyond the restricted assumptions of classical planning, in most realistic applications simply making plans is not enough. Planning, as a search over predicted state changes, uses descriptive models to abstractly describe what actions do. Acting, as an adaptation and reaction to an unfolding context, requires operational models which tell how to perform actions with rich control structures for closed-loop online decision making. The problem is how to maintain consistency between the descriptive and operational models.

In APE: An Acting and Planning Engine, Professor Dana Nau (CS/ISR) and his colleagues have developed Acting and Planning Engine (APE), an integrated acting-and-planning system that addresses the consistency problem by using the actor’s operational models both for acting and for planning. The paper appears in Advances in Cognitive Systems 7, December 2019. In addition to Nau, the authors include Sunandita Patra (CS/ISR); Malik Ghallab of the Centre national de la recherche scientifique (CNRS), France; Paolo Traverso of the Fondazione Bruno Kessler (FBK), Trento, Italy.

APE uses hierarchical operational models to choose its course of action with a planner that uses Monte Carlo sampling over simulated executions. A collection of refinement methods offers alternative ways to handle tasks and react to events. Each method has a body that can be any complex algorithm. In addition to the usual programming constructs, the body may contain commands (including sensing commands), which are sent to a platform that executes them in the real world. The body also may contain subtasks, which can be refined recursively.

APE’s acting engine is based on an expressive, general-purpose operational language. To integrate acting and planning, APE extends a reactive acting algorithm to include a planner called APE-plan.  At each point where it must decide how to refine a task, subtask, or event, APE-plan performs Monte Carlo rollouts with a subset of the applicable refinement methods. At each point where a refinement method contains a command to the execution platform, the module takes samples of its possible outcomes using a predictive model of what each command will do.

The authors’ experiments addressed multiple aspects of realistic domains, including dynamicity and the need for run-time sensing, information gathering, collaboration, and concurrent tasks. While APE shows substantial benefits in the success rates of the acting system, in particular for domains with dead ends, the authors are already designing, implementing and testing refinements to their system.

Related Articles:
Planning and learning algorithms developed for refinement acting engine
Clark School team wins AFRL funding for swarm autonomy planning and metareasoning
A cooperative control algorithm for robotic search and rescue
RoadTrack algorithm could help autonomous vehicles navigate dense traffic scenarios
Alum Evandro Valente testifies on two economic development bills
IFIG framework helps robots follow instructions
A learning algorithm for training robots' deep neural networks to grasp novel objects
ISR alum honors former advisor Benjamin Kedem with quantile frequency analysis paper
Who's walking deceptively? Manocha's team thinks they know.
AlphaGo family of AI programs grew from AMS simulation-based algorithms developed at UMD

February 17, 2020

«Previous Story  



Current Headlines

Reliability Engineering Ph.D./ECE M.S. student Paul Watrobski and colleagues write NIST IoT devices white paper

Abed on NSF Engineering CAREER Proposal Writing Workshop Committee

2020 Energy Seed Grants Awarded

Former post-doc Pomerantseva earns tenure at Drexel

In Race With Virus, Researchers Speed Development of Medical Equipment

GAMMA Group's Research on Emotional Modeling and Social Robotics Featured in Forbes

Srivastava wins NSF funding for integrated circuit fabrication security

Protection Collections Abound for Local Health Care Workers

New U.S. Patent: Integrated Onboard Chargers for Plug-In Vehicles

Public health planners: Free resources for emergency health clinics

Back to top  
Home Clark School Home UMD Home