Incomplete Domain Models, Uncertain Users and Open Worlds: Model-Lite Planning for Autonomy

Subbarao Kambhampati
Arizona State University

Endowing an automated agent with the ability to "plan" -- i.e., convert
its high-level goals into an executable course of action -- has been a
long-standing quest in Artificial Intelligence. For much of the
history of automated planning, the dominant research theme has been
efficient synthesis of plans under increasingly expressive system
dynamics (classical, temporal, stochastic etc.). An implicit
assumption underlying this research has been that of complete

In contrast, real-world planners are often faced with incompleteness
in domain models and/or goal/preference specifications. Such
incompleteness can arise from fallible domain writers, uncertain users
or open world scenarios faced by robotic agents. It poses new
challenges to plan synthesis, plan execution and model learning.

In this talk I will motivate the need for handling incomplete
specifications in planning, and describe the challenges foregrounded
by such "model-lite" planning. I will start with the need for novel
solution concepts for planning. I will present diverse plans and
robust plans as the appropriate solution concepts for partially
specified goals/preferences, and partially specified domain models
respectively. I will then outline our progress towards efficient
planners to support these solution concepts. I will also present open
world quantified goals as a way of handling goal and model
incompleteness together, and discuss how they are used in a planner
that controls a robotic agent in a disaster rescue scenario.

Bio: Subbarao Kambhampati is a professor of computer science and
engineering at Arizona State University, where he directs the Yochan
research group. His research and teaching interests are broadly split
between automated planning and intelligent information integration.
Kambhampati is the recipient of an NSF Research Initiation Award
(1992), an NSF Young Investigator Award (1994), a College of
Engineering Teaching Excellence Award (2002), and is an elected Fellow
of AAAI (2004). He served on the executive councils and co-chaired the
flagship conferences for both ICAPS and AAAI. His web site is

Presentation (PDF File)

Back to Machine Reasoning Workshops III & IV: Mission-Focused Actions/Reactions Based on & System Integration of Information Derived from Complex Real-World Data (by invitation only)