Publications

Contents

• Qualitative Spatial Reasoning
• AI In Conceptual Design
• Temporal Reasoning (Interval Algebra)
• Virtual Reality
• Qualitative Vision
• Conflicts in Behaviours
• Robotic Self-Calibration
• Neural Motor Control
• Computer Integrated Manufacturing

1. Mukerjee, Amitabha, 1996
   Querying Spatial Features by Shape,
   International Journal on Geographical Information Systems, Submitted.

2. Mukerjee, Amitabha, and Hernandez, Daniel, 1995
   Representing Spatial Knowledge: A tutorial,
   International Joint Conference on AI, IJCAI-95 Montreal, 1995, 112 pages. Contents

3. Mukerjee, Amitabha, Manish Agrawal, Praveen Bhatia, 1995
   A qualitative discretization for 3D contact motions,
   International Joint Conference on AI, IJCAI-95 Montreal, 1995. Postscript File
   • How to think qualitatively about a a cube or a hexagon rolling down a slope? Instead of thinking of shape in isolation, this work attempts to think of the shape of an object in terms of its interaction with other objects. Develops a qualitative decomposition of the contact motion space for two 2D objects, which can roll/slide into qualitatively different contact formations.

4. Mittal, Nishant, and Mukerjee, Amitabha, 1995
   Qualitative Subdivision Algebra: moving towards the Quantitative
   UT Austin CS Dept TR 95-25 / IIT Kanpur TR ME-95-011 Postscript File
   • Continuous parameters such as distance, aspect ratios, angles, etc. have to be discretized into zones over a qualitative interval. Composition in such Qualitative/quantitative hybrid models is seriously affected by the nature of this discretization. In this paper we investigate some discretizations for the add and multiply operations and demonstrate a class of k-proper algebras that result in minimal loss of information.

5. Mukerjee, Amitabha, and Gene Joe, 1990
   A qualitative model for space.
   Proceedigns of the AAAI-90, July 29-Aug 3, Boston, p.721-727.

6. Mukerjee, Amitabha, and Gene Joe, 1992
   A qualitative model for space.
   Texas A&M University CS Dept, TR 92-003, 70 pages.
   • This TR is a synthesis of the early work in this group involving the basic 2-D position model with angles, and the full transitivity tables. It includes three applications: Document Recognition using 2-D projective primitives, Qualitative Path Planning, and a Voronoi Model of shape. It can be obtained by e-mail .

7. Mukerjee, Amitabha, and Gene Joe, 1989
   Representing spatial relations between arbitrarily oriented objects.
   International Conference on Machine Intelligence and Vision (MIV-89), Tokyo Japan, April 1989, p.288-291. Available as Tech Report TR 89-019 from TAMU-CS.

8. Mukerjee, Amitabha, 1989
   Getting beneath the geometry: a systematic approach to modeling spatial relations,
   Proceedings of the Workshop on Model-Based Reasoning, IJCAI-89, Detroit, Aug. 20, 1989, ed. Ethan Scarl, p.140-141.

9. Mukerjee, Amitabha, 1989
   A representation for modeling functional knowledge in geometric structures,
   In Knowledge Based Computer Systems, Springer-Verlag Notes in Artificial Intelligence, ed. S. Ramani, Proceedings of the KBCS '89 conference, Bombay, December 1989, p.375-384.

10. Mukerjee, Amitabha, 1994
    Metric-less modeling of one, two and three-dimensional metric spaces,
    Working notes of the AAAI Workshop on Spatial and Temporal Reasoning, AAAI-94 , Seattle 1994, p.39-45. Also IIT Kanpur Dept of Mech. Engg,, Technical report ME-94-026.

11. Mukerjee, Amitabha, and Samuel E. Bratton, 1991
    A qualitative model for spatial learning,
    Avignon '91, Expert Systems and their Applications, May 27-31, 1991.

AI IN CONCEPTUAL DESIGN:

1. Agrawal, Ram Bhushan, Amitabha Mukerjee, and Kalyanmoy Deb, 1995
   Modelling of Inexact 2-D Shapes using Real-coded Genetic Algorithms,
   Proceedings of the Symposium on Genetic Algorithms, March 25, 1995, Dehradun, India, p.41-49, ed. Pradosh K. Roy and S.D. Mehta, Bhishen Singh Mahendra Pal Singh publishers. Postscript File
   • Shape models at different resolutions and multiple approximation models (Idealization). 2D sketches represent a class of objects, from which samples can be displayed for the user as visual feedback, and can be directly used for Optimization at the conceptual, pre-detailed level. Different parts of the Sketch represent different accuracies.

2. King, Joseph Scott, and Amitabha Mukerjee, 1990
   Inexact Visualization,
   Proceedings of the IEEE Conference on Biomedical Visualization, Atlanta GA, May 22-25, 1990, p.136-143. Also TAMU CS TR 90-002
   • A 3D model of shape that allows for reconstruction while retaining shape abstraction capability.

3. Mukerjee, Amitabha, and Sarvesh Srivastava, 1994
   Designing a Class of Geometries - From Concept to Instantiation
   IIT Kanpur Dept ME, TR ME-94-025. Postscript File
   • How to model geometric designs in the conceptual stage, and incrementally refine it to the final stage. The user defines a class of possible shapes and geometric interrelations, and the systems defines a parametric model. Then a set of functional constraints are given and the parametric model is instantiated and a set of feasible results identified using Genetic Algorithms. Test results shown for some scooter Crankshaft designs.

4. Mukerjee, Amitabha, and Nishant Mittal, 1994
   A qualitative representation of frame-transformation motions in 3-dimensional space
   Proceedings of the IEEE Conference on Robotics & Automation, Nagoya, Japan 1995, Longer version in IIT Kanpur Dept of Mechanical Engg, Technical report ME-94-026.

5. Mukerjee, Amitabha, 1991
   Qualitative geometric design,
   Solid Modeling Foundations and CAD/CAM Applications, ACM/SIGGRAPH Symposium, Austin, June 5-7, 1991.

TEMPORAL REASONING (INTERVAL ALGEBRA)

1. Kumar, Krishna, and Amitabha Mukerjee, 1987
   Temporal Event Conceptualization.
   Proceedings of the Seventh International Joint Conference on Artificial Intelligence, IJCAI-87 Milan, Italy, August 1987. Morgan Kaufmann.

2. Mukerjee, Amitabha, and Frank Schnorrenberg, 1991
   Reasoning across scales in space and time,
   AAAI Symposium on Principles of Hybrid Reasoning, November 15-17, 1991, Asilomar, CA. Postscript File
   • When information known at one scale has to be integrated with knowledge at a different scale, one needs to deal with intervals with different tolerances. This work relates these tolerances to the scale of current discourse and develops transitivity tables for such operations.
3. Mukerjee, Amitabha, and Krishna Kumar, 1987
   A Plan Recognizer as an Independent Observer for Autonomous Systems.
   Fifth International Symposium on Unmanned Untethered Submersible Technology, Durham, New Hampshire, October 1987.

VIRTUAL REALITY

1. Mishra, N.K., Singh, M.P., Prasannaa, T.V., Birla, B.K., Vidhani, D., Lal, A.N., and Mukerjee, Amitabha, 1995
   Gesture based user interfaces: An alternative model for virtual reality,
   International Conference on Cognitive Systems, New Delhi, India, December 1995.
   • See this web page on Gesture Based Virtual Reality.

2. Singh, M.P., Mishra, N.K., and Mukerjee, Amitabha, 1996
   The Virtual Director
   Conference of the Computer Society of India, (CSI-96) Bangalore, India, October 1996.

3. Mishra, N.K., Singh, M.P., Prasannaa, T.V., Birla, B.K., Vidhani, D., Lal, A.N., and Mukerjee, Amitabha, 1996
   Experiments in Gesture Based User Interfaces,
   Conference of the Computer Society of India, (CSI-96) Bangalore, India, October 1996.
   • We present a suite of four applications which highlight the versatility of vision as a single platform for implementing gesture analysis: a Virtual Reality game, a CAD input interface, an Overhead Crane controller, and a Master-Slave Robot teleoperator. Unlike other gesture systems like the dataglove or the bodysuit, these applications span the space from coarse to fine motions, static to dynamic gestures, etc. In themselves also, these applications hold great interest since gestures are likely to become a key component of spatial interaction with the devices of tomorrow.

ROBOTICS: BEHAVIOURS

1. Mali, Amol D., and Mukerjee, Amitabha, 1994
   Robot behaviour conflicts: can intelligence be modularized?
   Proceedings of the AAAI-94, p.1279-1284
   • One of the key notions here is the deveDecentralized models of Intelligence. A study of behaviour based robots (reactive systems such as Brooks' Subsumption architecture) finds conflicts occurring due to a cyclic chain in the calling of events.

2. Mukerjee, Amitabha, and Mali, Amol D., 1994
   Reactive Models of Intelligence - Limitations and Prospects
   Dept of Mechanical Engg, IIT Kanpur, Tech Report ME-94024 Postscript File
   • Reactive models of intelligence are based on independent behaviour modules, each intermediating directly between the input stimulus and the output action. However, cyclic conflicts are known to arise where a chain of behaviours executes endlessly in a loop. We investigate the limitations highlighted by this, and present some new results on the complexity of tasks that can be solved using the reactive paradigm. This work includes an analysis of its effect on learning..

3. Mukerjee, Amitabha, Sanjay Sharma, and Ram Bhushan Agrawal, 1995
   When is an Obstacle a Perfect Obstacle?
   Proceedings of the IEEE Conference on Robotics & Automation, Nagoya, Japan 1995, Also IIT Kanpur Dept of Mechanical Engg, Technical report ME-94-026.
   • Most path planning operates on the assumption that all obstacles are inviolable i.e. these are infinite cost zones. Such obstacles are called Perfect. By looking at the shapes of the obstacles, one can determine the conditions under which this is strictly true. .

ROBOTIC SELF-CALIBRATION

1. Mukerjee, Amitabha, 1984
   Adaptation in biological sensory-motor systems: A model for robot control.
   Proc. SPIE Conference on Intelligent Robots and Computer Vision, Cambridge, Mass. November 1984, SPIE vol. 521, p. 243-247.

2. Mukerjee, Amitabha, 1986a
   Self-calibration strategies for robot manipulators.
   Ph.D. Dissertation, University of Rochester, March 1986, CS Dept. TR-1920.

3. Mukerjee, Amitabha, 1986b
   Robot learning: transferring trajectory control information between similar trajectories.
   Texas A&M Univ CS Dept TR-87-003.

4. Mukerjee, Amitabha, 1987
   Learning to be Mobile: Robotics in Artificial Intelligence.
   Eleventh Computers at the University Symposium, Cavtat, Yugoslavia, June 1987.

5. Mukerjee, Amitabha, 1988
   Joint Force Sensing for Unified Robot Calibration.
   In Sensor Devices and Systems for Robotics, ed. Alicia Casals, Springer NATO ASI Series v. F52.
6. Mukerjee, Amitabha, Benson, Richard C., and Ballard, Dana, 1984
   Towards self-calibration in robot manipulator systems: dynamics enhancement through trajectory deviation analysis.
   Proc. 7th Symposium on Engg. Applications of Mechanics, Toronto, June 1984, p.74-81.

7. Mukerjee, Amitabha, and Dana Ballard, 1985
   Self-calibration in robot manipulators.
   IEEE International Conference on Robotics and Automation, St. Louis, March 1985, ICRA-85, p.1050-1057.