Research Statement
Subbarao Kambhampati
http://rakaposhi.eas.asu.edu/rao.html (rao@asu.edu)
My primary research interests are in Artificial Intelligence, particularly the areas of Automated Planning,
Scheduling and related problems such as constraint satisfaction. In the last four years, I have also developed a
strong second interest in the intersection of AI and Databases–particularly in the area of Intelligent Information
Integration. I will provide a summary of my research in both these areas.
Planning
Planning, the ability to formulate a course of action to satisfy some desired goals, is a core problem in designing
intelligent agents. The quest for effective action representations, and efficient plan synthesis frameworks has
been an active area of research for over thirty years. In the last ten years, I have spent significant effort in both
strengthening the foundations of automated planning systems, and scaling up their ability to handle complex
problems. I have built a very active and visible research group at ASU, and together we developed several
state of the art planning algorithms and systems, and investigated their applications to problems in automated
manufacturing and space autonomy. The results of our work have appeared at top-notch publication forums
(including 15 articles in AIJ and JAIR—the premier archival journals in AI; and 40 papers in highly selective
AI and Planning conferences).
My interest in automated planning goes back to my 1989 Ph.D. dissertation, in which I developed a systematic framework for plan reuse called PRIAR. This work resulted in a well-cited paper in Artificial Intelligence,
and was also the basis for my ACM Samuel Alexander Dissertation award. During 1989-91, as a research
associate at Stanford, I was involved in applying the techniques from planning and plan reuse to process planning in automated manufacturing. I joined ASU in 1991, and during 1991-94, I focused on speeding up the
existing planning techniques through explanation based and case-based learning techniques. Two systems–
UCPOP+EBL and DerSNLP–that resulted from this work were among the fastest plan synthesis systems at
that time. This work was in part responsible for my 1994 NSF Young Investigator award. During 1993-95, I
worked on bringing structure to a mushrooming, and on the surface incomparable planning algorithms. My
work on foundations of refinement planning formalized the semantics of a partial plan in terms of its candidate
set, and characterized the various existing planners in terms of different ways of splitting the plan candidate
set. I gave an invited talk on this unifying framework at the 1996 AAAI (national conference on AI), and a
paper based on this work, that appeared in the Artificial Intelligence journal, is a highly cited one.
In the last five years, my research on automated planning focused on understanding the sources of strength
behind disjunctive planners and exploiting those insights to develop novel scalable disjunctive as well as conjunctive plan synthesis algorithms for a variety of planning problems. My research group and I have made
substantial progress on these ambitious objectives. Some of the important recent research contributions of my
group in include: (i) Explicating a class of planners called “disjunctive planners” and showing that Graphplan
and SATPLAN style planners are best understood as disjunctive planners, (ii) Highlighting the intimate connections between disjunctive planners and combinatorial workhorses such as CSP and integer programming,
and exploiting those connections to develop novel scalable planners—including GP-CSP, which casts planning
as constraint satisfaction, and GP-EBL which extends explanation-based learning techniques to Graphplan (iii)
Showing that the optimistic reachability information inherent in the planning graphs provides a very rich basis
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for deriving highly effective heuristics, and exploiting this idea to develop a variety of planners with state-ofthe-art performance, including: AltAlt a classical regression planner; RePOP, a partial order planner; Sapa a
multi-objective metric temporal planner; and CAltAlt, a conformant planner and (iv) Developing a loosely coupled architecture for integrating planning and scheduling techniques and implementing it in RealPlan system.
This work has had a wide-ranging impact on the planning community. The disjunctive planning framework has become one of the standard ways of looking at the new directions in plan synthesis. I gave very
well-attended tutorials at IJCAI 1999, AAAI 2000 and Planet Summer School on Planning (2000). The connections between Graphplan and CSP, that I explicated, have since been exploited gainfully by a variety of
other researchers. AltAlt bridged the upto-then disjoint worlds of disjunctive planners and heuristic search
planners by showing that planning graphs—the heart of Graphplan—can be used to derive heuristics that allow it to outperform both Graphplan and heuristic search planners. By showing how the strengths of the state
of the art planners can be generalized beyond state-search planners, RePOP revived interest in partial order
planning, a flexible plan synthesis framework, that was all but given up for dead with the advent of disjunctive
planners. Sapa exhibited the best performance for the most expressive metric-temporal planning problems
in the 2002 planning competition. RealPlan is among the select set of planners that effectively integrates
techniques from planning and scheduling communities.
My decade-long work in automated planning exhibits some common methodological threads. I am strongly
interested in building planning systems with sound theoretical underpinnings. In addition to building them, I
also want to understand as clearly as possible the tradeoffs offered by those algorithms, and to exploit their
synergies. I believe that we truly understand techniques only when we can tear them apart and leverage their
sources of strength to develop significantly better hybrid techniques. Consequently, I avoid the tempting but
largely fruitless brand-name and black-box comparisons, and strive for a deeper understanding of relative
advantages of different ways of tacking the search challenges in the planning problems. My earlier work on
refinement planning framework to unify the multitude of partial order planners, my recent work on combining
the disjunctive and heuristic search planning paradigms to develop algorithms superior to both, as well as my
work on reviving partial order planning are good exemplars of this research methodology.
I have been an active member of the planning community. I co-chaired the 2002 International Conference
on AI Planning Systems (AIPS), and served on the AIPS executive council. In this latter capacity, I played
an important role in merging the European Planning Conference and AIPS to start a new conference called
ICAPS (International Conference on AI Planning and Scheduling). I wrote several survey articles on automated
planning techniques and gave well-received tutorials at IJCAI and AAAI. Over the years, my research on
automated planning has been supported by a variety of competitive federal research grants, including three
from NSF (including the 1994 NSF Young Investigator award), three from DARPA, two from NASA and one
from AFRL.
In the coming years, I plan to focus on (i) robust and efficient techniques for temporal planning that
integrate ideas from planning, scheduling and operations research (ii) scalable approaches for dealing with
stochastic and partially observable domains (iii) planning in multi-agent scenarios and (iv) applications of
planning and plan recognition techniques to intelligent user interfaces, and biological pathway discovery (in
addition to those in manufacturing and space autonomy).
Information Integration
The availability of structured information sources on the web has recently lead to significant interest in query
processing frameworks that can integrate information sources available on the Internet. The impact of such
integration framework is clear: effective integration of data and information sources has been cited as the most
pressing need (and challenge) in spheres as diverse as “Counter-terrorism and Homeland Security” and the
Human Genome project. Since 1998, I have been an active researcher in data and information integration.
I was initially attracted to this area by the connections between query planning in information integration
and automated planning with “sensing” (information-gathering) actions. My interests in the area have since
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expanded considerably and I now have three students working with me on adaptive frameworks for information
integration. I am also an active participant and a leader among an inter-disciplinary group of faculty at ASU
involved in developing enabling technologies for intelligent information integration.
My current work in this area is in the context of the Emerac and Havasu projects, and tackles many open
challenges in designing adaptive information integration frameworks, including: (a) automatically gathering
source meta-data and (b) using the gathered meta-data to support multi-objective query processing. The former
is important because the sources in information integration tend to be autonomous and rarely export enough
meta-data about their data profile or delivery parameters to support effective query processing. The latter
is important as unlike query processing in traditional databases, in information integration scenarios users
often have multiple and conflicting objectives between coverage (the number of answers returned), cost (the
execution time taken), rate (the rate at which the answers are returned) as well as the quality of the answers. We
are developing approaches based on ideas from datamining and machine learning, to efficiently learn source
meta-data (starting with coverage and overlap statistics) and use the gathered meta-data in the context of novel
multi-objective query optimization techniques.
Given my hybrid research background, I have also been involved in fostering closer connections between
AI and database research communities. I was on the program committee of SIGMOD 2000, and also proposed
and co-taught a successful tutorial on Web-based information integration at the 2002 National Conference
on Artificial Intelligence. I will be co-chairing a 2-day workshop on intelligent information integration at
International Joint Conference on AI, specifically aimed at bringing together researchers from AI and Database
communities working on information integration.
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Statement on Teaching and Mentoring
Subbarao Kambhampati
http://rakaposhi.eas.asu.edu (rao@asu.edu)
Of the many comments I had received from my students in my teaching career, the ones that I treasure most are
those that go: “His courses are demanding and I had to work much harder. But, I learned more.” I am gratified by
such comments because I believe that learning is great fun -- but not as a spectator sport. As my course homepages
will show, my courses are very demanding both on my, and my students’ time. As a teacher, I see my primary duty
as using my talents (if any) to make the demanding courses also “fun” and engaging courses. I try to accomplish
this in several ways.
First and foremost, I go out of the way (and sometimes out on the limb) to showcase my own enthusiasm for the
subject that I am teaching. I use many techniques—humor, off-the-wall analogies, interaction, catchy visual aids—to
keep the classes lively. Second, I work hard in designing my courses, updating them significantly every time I teach
them. I also attempt to connect the material being taught to the material they would have learned in the past, as well
as the many places in which they will be using it in the future. Third, I maintain multiple communication channels
with the students. I view the course as a semester long contract with the students, rather than as a twice-weekly
contact with them. I see classroom as just the beginning of my contact. I use the Web, the class homepage and email lists extensively to keep in touch with my students' minds even when they are not in the class room. Fourth, I
give the students several opportunities to provide anonymous feedback on the conduct of the course during the
semester. Finally, avoiding the midterm/final model, I provide the students many and varied opportunities to
demonstrate their mastery of the material.
My methods seem to work reasonably well for me. Despite the reputation my courses have acquired of being
challenging and demanding, I have consistently received good teaching evaluations. I have been nominated several
times for the college-level teaching awards, and received the 2002 College of Engineering Teaching Excellence
Award.
Course Development and Pedagogical activities: I have developed and taught two courses on automated
planning---one focusing on deterministic problems, and the other on stochastic and partially observable ones. The
notes from these courses have developed quite a bit of following in the planning community. More recently, I
developed and taught twice a popular senior level course on Information Retrieval, Mining and Integration on the
Internet. In addition to these new courses, I had continually revamped the contents of the senior Introduction to AI
course, to keep pace with a rapidly maturing field.
The specific ways of teaching particular topics that I develop in the classroom often make their way into tutorials
and survey papers. As an example, I have written several tutorial articles, and delivered two well received tutorials
on automated planning, and one on information integration—based to a large extent on the notes I developed in my
graduate level courses. Cora, a Computer Science research paper search engine, lists six of my survey papers among
the top ten influential survey papers in automated planning. I am currently involved in writing a textbook on the
foundations of automated planning and scheduling (to be published by Prentice Hall).
Graduate Student Mentoring: If I have been successful in my research program at ASU, it is to a large extent
thanks to the quality of graduate students I have had the pleasure work with. I have always spent a considerable time
recruiting high-quality students into ASU graduate program (and my lab). I attempt to inspire/encourage my
graduate students in many ways, including getting them into contact with researchers at other schools, and
supporting their travel to national conferences. All my MS and Ph.D. students are encouraged to do research that is
publishable in high quality conferences and journals. I have over 60 technical publications co-authored with my
students. This track record has brought significant international recognition to my research group as a whole.
Four students completed their Ph.D. and eleven students completed their Masters degrees under my supervision.
One of my Ph.D. students, Amol Mali, is a tenure-track faculty member at University of Wisconsin-Milwaukee.
Another student, Biplav Srivastava, is with IBM Research Labs, India. I am currently advising eight students—six in
the Ph.D. program and 2 in the M.S. program.