http://www.cse.ohio-state.edu/~chandra/Chandra.JPGB. Chandrasekaran

Professor Emeritus, Senior Research Scientist

How do you know but ev'ry bird that cuts the airy way
Is an immense world of delight, clos'd by your senses five?

William Blake

Professional Data

Effective July 1, 1995, I am a Professor Emeritus in the Computer and Information Science Department. Effective October, I have an appointment as Senior Research Scientist and I will continue to be Director of The Ohio State University Laboratory for AI Research (LAIR).

My retirement plans: No retirement. Full-time research. Retirement has reduced the number of excuses I have for not implementing my writing plans.

My address, etc. 


Email: chandra@cse.ohio-state.edu
Office: 591 Dreese Lab
Phone: (614) 292-0923 (has answering machine)
Mail:
Laboratory for Artificial Intelligence Research
Department of Computer and Information Science
395 Dreese Lab
2015 Neil Avenue
Columbus, OH 43210-1277 USA
Fax: (614) 292-2911 (CSE) (Make sure my name is written prominently on the front page as the recipient.)

Home: 2053 Iuka Avenue, Columbus, OH 43201 (614) 297-0470 (has answering machine)
Directions to my house: http://www.cse.ohio-state.edu/~chandra/Directions-to-2053-Iuka-Ave.htm

My CV: Click here for my CV. 


AI-interested applicants to Graduate Programs, please click here for information before writing to me

My Research Interests

Primarily, my research interest is in understanding intelligence from a computational perspective. I am particularly interested in intelligence as expressed in high-level cognitive activities such as problem solving, understanding, and explanation.

The LAIR research team has concentrated its efforts on creating theories about intelligence, in addition to building socially, technologically useful tools which embody these theories. We have been analyzing diagnostic and design tasks, which in turn, have led us to a study of how causal processes are understood and used. I have also begun research in how visual representations are used during problem solving. All this research is done in various real-world domains; engineering and medicine provide us with most of our challenges.

Selected Recent Publications

My recent focus has been mostly on issues related to diagrammatic reasoning.  See the section below on this topic.  I have also been doing quite a bit of work experimenting with use of our  Seeker-Filter-Viewer architecture for multi-criterial decision-making, specifically for Course of Action planning and as a data mining tool to understand a decision space.  See the section below on this topic.  

Click here for a selected list of past publications by me and co-authors, organized by subject. 

The following recent papers/drafts can be downloaded. Comments are solicited.

Diagrammatic Reasoning, Multi-modal Cognitive Architectures

B. Chandrasekaran, "When is a bunch of marks on paper a diagram? Diagrams as homomorphic representations," Semiotica 186-1/4 (2011), 69-87.

 

B. Chandrasekaran, B. Banerjee, U. Kurup & O. Lele, "Augmenting Cognitive Architectures to Support Diagrammatic Imagination," Topics in Cognitive Science (2011) 1-18.

 

B. Banerjee and B. Chandrasekaran. “A Constraint Satisfaction Framework for Executing Perceptions and Actions in Diagrammatic ReasoningJournal of Artificial Intelligence Research, 39, pp. 373-427, 2010

B. Banerjee and B. Chandrasekaran. (2010) "A spatial search framework for executing perceptions and actions in diagrammatic reasoning." In Diagrammatic Representation and Inference, A. K. Goel, M. Jamnik and N. H. Narayanan, Editors, Lecture Notes in Artificial Intelligence 6170, Springer, Heidelberg, pp. 144-159.

B. Chandrasekaran and Omkar Lele, "Mapping Descriptive Models of Graph Comprehension into Requirements for a Computational Architecture: Need for Supporting Imagery Operations," In Diagrammatic Representation and Inference, A. K. Goel, M. Jamnik and N. H. Narayanan, Editors, Lecture Notes in Artificial Intelligence 6170, Springer, Heidelberg, pp. 235-242.

Unmesh Kurup & B. Chandrasekaran, “A Cognitive Map for an Artificial Agent,” Proceedings of Artificial General Intelligence (AGI-09) Conference, Arlington, VA, March 6-9, 2009.

 

B. Chandrasekaran, "Multimodal Cognitive Architecture: Making Perception More Central to Intelligent Behavior," Proceedings of the AAAI National Conference on Artificial Intelligence, 2006, pp. 1508-1512

B. Chandrasekaran, “Diagrams as Physical Models,”  in Diagrammatic Representation and Inference, LNAI 4045, Dave Barker-Plummer, Richard Cox and Nik Swoboda, editors, Springer, 2006, pp. 204-217.

 

B. Chandrasekaran, Unmesh Kurup, Bonny Banerjee, John R. Josephson  and Robert Winkler, “An Architecture for

Problem Solving with Diagrams,” in Diagrammatic Representation and Inference, Alan Blackwell, Kim Marriott and Atsushi Shomojima, Editors, Lecture Notes in Artificial Intelligence 2980, Berlin: Springer-Verlag, 2004,  pp. 151-165.

.Click Diagrammatic Reasoning to look at a description of a recent book that I co-edited.

 

 

Decision Support Systems

Architecture for Multi-Criterial Decision Assistance

 

We developed an architecture for multi-criterial decision assistance, which was first applied to exploring large design spaces -- see paper by Josephson et al, in the section on Design.  But, the recent emphasis has been on other decision problems such as planning.  Here is a  recent paper.  

Richard Kaste, Janet O’May, Eric Heilman, B. Chandrasekaran and John Josephson, FROM SIMULATION TO INSIGHTS: EXPERIMENTS IN THE USE OF A MULTI-CRITERIAL VIEWER TO DEVELOP UNDERSTANDING OF THE COA SPACE, Proceedings of US Army Research Laboratories Collaborative Technology Alliances Symposium, April 29-May 1, 2003, University of Maryland Conference Center, College Park, MD. 

There is an in-principle gap between simulation models and reality.  This suggests that the real challenge is not optimal decisions as much as robust decisions, robust in the face of deep uncertainties, Rumsfeldian “unknown unknowns.”  This view is the basis of:
 B. Chandrasekaran, “Designing Decision Support Systems To Help Avoid Biases & Make Robust Decisions, With Examples From Army Planning,” Proceedings of the 2008 Army Science Conference.
A somewhat dated white paper on how one might think about these issues: B. Chandrasekaran, From Optimal to Robust COAs: Challenges in Providing Integrated Decision Support for Simulation-Based COA Planning, A White Paper, Feb 2005.  Here is a further elaboration of some of these ideas:

“Decision-Making and the Cognitive Architecture of Problem Solving,” Keynote Talk, First IEEE Symposium on Computational Intelligence in Multi-Criteria Decision-Making (MCDM 2007), Honolulu, HI, April 3, 2007.

 

Cognitive Science/AI Foundations

 

B. Chandrasekaran, and Susan G. Josephson, "Separability Hypothesis," (pdf), draft of paper arguing that AI is better off to have as its working hypothesis that it is concerned only with cognitive functions rather than accounting also for subjectivity and consciousness.

 

Task Structures, Ontologies

 

This recent paper describes an evolution in my view of knowledge and its representation.

B. Chandrasekaran, “Problem Solving Methods and Knowledge Systems: A Personal Journey to Perceptual Images as KnowledgeArtificial Engineering in Engineering Design and Manufacturing, Special Issue on Problem Solving Methods: Past, Present and Future, 2009, Vol. 23, No. 3, 331–338.

B. Chandrasekaran, Generic tasks in knowledge-based reasoning: High-level building blocks for expert systems design, IEEE Expert, 1, 3, 23-30, 1986.

Johnson, T. R., Smith, J. W., and Chandrasekaran, B. (1992). Task-specific architectures for flexible systems. In P. S. Rosenbloom, J. E. Laird, & A. Newell (Eds.), The Soar Papers: Research on Integrated Intelligence. The MIT Press. 

B. Chandrasekaran, T. Johnson, and J.W. Smith, "Task Structure Analysis for Knowledge Modeling," Communications of the ACM , Vol. 33, No. 9, September 1992, pp. 124-136; anthologized in Knowledge Oriented Software Design, ed., J. Cuena, North-Holland (Amsterdam), 1993, pp. 1-22.

Chandrasekaran, B. & Johnson, T. R. (1993). Generic tasks and task structures: History, critique and new directions. In J.-M. David, J.-P. Krivine, & R. Simmons (Ed.), Second Generation Expert Systems (pp. 232-272). Berlin: Springer-Verlag.

B. Chandrasekaran, "Intelligent Control at the Knowledge Level," (Acrobat) which was presented at the 1994 AAAI Fall Symposium. This paper seeks to answer the question, "What is common as a task between the thermostat and Allen Greenspan, both of them engaged in the task commonly called 'control'?" The paper is really pretty schematic and awaits a longer treatment.

B. Chandrasekaran , J. R. Josephson and V. R. Benjamins, "Ontology of Tasks and Methods," (Acrobat),  1998 Banff Knowledge Acquisition Workshop.  The first part of the  paper appears in  "What are ontologies and why do we need them?," IEEE Intelligent Systems, Jan/Feb 1999, 14(1), pp. 20-26. 

Functional Representation

B. Chandrasekaran and John R. Josephson, "Function in Device Representation," Engineering with Computers, Special Issue on Computer Aided Engineering, (2000) 16:162-177.  In this paper, we finally got a chance to set down a whole bunch of ideas on device representation, especially on how to be clear about what one means by terms such as structure, behavior and function.  We also relate how devices arise in response to needs in the world, and how needs are transformed into device functions.  This builds on our earlier paper on "Representing Function as Effect" (see below), and makes connections with the earlier FR work.  

B. Chandrasekaran and J. R. Josephson "Representing Function as Effect", (Acrobat) Preprint of paper presented at AAAI-96 Workshop on Modeling and Reasoning about Function, Portland, OR, August 1996 . This is an improved version of a part of another draft, "An Explication of Function", (postscript) which, in addition to the discussion about function, also has a discussion about causal explanation in general, how causal ordering is a common form in which causal explanation is couched, and how the CPD of the FR framework is a specific type of causal ordering in which the dependencies of the properties of the device on the properties of the component are made explicit. Comments are invited on both papers.

B. Chandrasekaran, and H. Kaindl, "Representing Functional Requirements and User-System Interactions," (Acrobat) Preprint of paper presented at AAAI-96 Workshop on Modeling and Reasoning about Function, Portland, OR, August 1996.

        Design 

B. Chandrasekaran, "Design Problem Solving: A Task Analysis," AI Magazine, Vol. 11, No. 4, Winter 1990, pp. 59-71. Also appears (in Japanese), Nikkei Artificial Intelligence Quarterly , Summer 1991, pp. 142-154, and anthologized in Knowledge Aided Design, M. Green, ed., Academic Press, London, 1992, pp. 25-46.

John R. Josephson , B. Chandrasekaran , Mark Carroll , Naresh Iyer, Bryon Wasacz, Giorgio Rizzoni , Qingyuan Li , David A. Erb, An Architecture for Exploring Large Design Spaces, Proc AAAI-98.   This paper is a product of research under a recent DARPA project.

See the paper Multimodal Perceptual Representations and Design Problem Solving listed in the Diagrammatic Representation section above.

        The S-F-V Multi-Criterial Decision Architecture

It was first applied to exploring large design spaces, see paper by Josephson et al, in the section on Design.  But, the recent emphasis has been on other decision problems such as planning.  Here is a  recent paper.  

Richard Kaste, Janet O’May, Eric Heilman, B. Chandrasekaran and John Josephson, FROM SIMULATION TO INSIGHTS: EXPERIMENTS IN THE USE OF A MULTI-CRITERIAL VIEWER TO DEVELOP UNDERSTANDING OF THE COA SPACE, Proceedings of US Army Research Laboratories Collaborative Technology Alliances Symposium, April 29-May 1, 2003, University of Maryland Conference Center, College Park, MD. 

       Cognitive Modeling for Simulation

B. Chandrasekaran and John R. Josephson,  Cognitive Modeling For Simulation Goals: A Research Strategy for Computer-Generated Forces, presented at the  8th Conference on Computer Generated Forces and Behavioral Representation, 11-13 May 1999, Orlando, FL.   This paper argues that fidelity considerations are closely connected to the goals for which the simulation is intended, that current research often pursues fidelity independent of the task, and that there is a need to develop a systematic body of knowledge relating simulation tasks to the degree of fidelity required of the models.  The arguments make use of our earlier work on generic tasks and task structures.

Some Other Activities

I am on the editorial board of a number of journals, one of which is

The Journal of Experimental and Theoretical Artificial Intelligence

Personal Data

In this picture, our daughter, Mallika, is seen with her mother in the Yellow Mountain Region of China where we were on a visit in May 1995.

 
Last updated April 12, 2002.