talk on Online Continual Planning to Control Modular Production Printer (Minh Do; PARC Labs) 4/24 3:15pm BY 190

["Subbarao Kambhampati" <rao@asu.edu>]

Title: Online Continual Planning to control Modular Production Printer

Speaker: Minh B. Do,  (Xerox) PARC Labs, Palo Alto

Date/Time:  Thursday 4/.24  3:15pm  BY 190

Abstract: This talk summarizes the recent work at the Embedded
Reasoning System at PARC on applying automated planning techniques to
the control of modular production printing equipments. These
reconfigurable printers radically change the traditional design by
using simpler, interchangeable, but smarter components. Like many
other real-world applications, such as mobile robotics, this complex
domain requires real-time autonomous decision-making and robust
continual operation. To our knowledge, this work represents the first
successful industrial application of embedded domain-independent
temporal planning. Main challenges of applying automated planning
technology in this domain include compositional modeling, on-line
planning and exception handling, real-time planner control, and the
interaction with low-level controller. At the heart of our system is
an on-line algorithm that combines techniques from state-space
planning and partial-order scheduling. For example, our
planning-graph-based planning heuristic takes resource contention into
account when estimating makespan remaining. We suggest that this
general architecture may prove useful as more intelligent systems
operate in continual, online settings. Our system has been used to
drive several commercial prototypes and numerous hypothetical (but
realistic) printer configurations. When compared with
competition-winning state-of-the-art off-line planners in this domain,
our system is hundreds of times faster and often finds much better
quality plans. At the end of the talk, I will also discuss current
extensions of our current planning framework to other online planning
domains that share similar characteristics and also to objective
functions beyond the default maximization for machine productivity.



Bio: Minh Do is a Research Staff in the Embedded Reasoning Area at the
Palo Alto Research Center (formerly Xerox PARC). He graduated from the
Yochan planning group at Arizona State University in 2004 and has been
working on transferring his knowledge in offline domain-independent
metric temporal planning into fast online continual planning
applications.  Besides temporal and online planning, Minh Do has
worked on other planning topics such as over-subscription planning,
planning as CSP/ILP/SAT, and integrating planning and diagnosis. He
has published a few dozens papers, filed several patents on automated
planning and co-authored the ICAPS best application paper award on
planning for high-speed modular printer control. This year, he is
co-chairing the deterministic track of the 6th International Planning
Competition.