Diagrammatic reasoning for planning and intelligent control

We describe a possible approach to planning starting from an emerging AI subfield. The models we propose are based on diagrammatic representations for reasoning about dynamic aspects of the world. Diagrammatic knowledge representation is an approach to knowledge representation in AI programs, that is suitable for problem solving and reasoning in spatial domains. Our claim is that diagrammatic representations could offer a way to combine AI and control system techniques for intelligent planning and control. The reason is that diagrammatic representations can share the high-level features of AI formalisms, such as explicit representations of objects, events, and situations, but with a finer-grained decomposition of actions and shapes. The dynamic aspects of our models are based on the metaphor of abstract potential fields (APF).

[1]  David Chapman,et al.  Pengi: An Implementation of a Theory of Activity , 1987, AAAI.

[2]  P. Morasso,et al.  Kinematic networks , 1988, Biological Cybernetics.

[3]  Luc Steels.  Steps towards Common Sense , 1988, ECAI.

[4]  Pietro Morasso,et al.  Computational maps and target fields for reaching movements , 1997 .

[5]  Edoardo Ardizzone,et al.  A hybrid scheme for action representation , 1993, Int. J. Intell. Syst..

[6]  Tomás Lozano-Pérez,et al.  An algorithm for planning collision-free paths among polyhedral obstacles , 1979, CACM.

[7]  H. Gelernter,et al.  Realization of a geometry theorem proving machine , 1995, IFIP Congress.

[8]  A. H. Soni,et al.  An Algorithm for Computer Guidance of a Manipulator in Between Obstacles , 1975 .

[9]  J. Brian Burns,et al.  Path planning using Laplace's equation , 1990, Proceedings., IEEE International Conference on Robotics and Automation.

[10]  Oussama Khatib,et al.  Real-Time Obstacle Avoidance for Manipulators and Mobile Robots , 1986 .

[11]  Salvatore Gaglio,et al.  A Cognitive Architecture for Artificial Vision , 1997, Artif. Intell..

[12]  Marc G. Slack.  Navigation templates: mediating qualitative guidance and quantitative control in mobile robots , 1993, IEEE Trans. Syst. Man Cybern..

[13]  Christian Laugier,et al.  Achieving Dextrous Grasping by Integrating Planning and Vision-Based Sensing , 1995, Int. J. Robotics Res..

[14]  Achille C. Varzi,et al.  Some Pictures are Worth 2[aleph]0 Sentences , 2000 .

[15]  Herbert A. Simon,et al.  Why a Diagram is (Sometimes) Worth Ten Thousand Words , 1987 .

[16]  Kenneth D. Forbus.  Qualitative Reasoning About Space and Motion , 1983 .

[17]  Brian V. Funt,et al.  Problem-Solving with Diagrammatic Representations , 1980, Artif. Intell..

[18]  Pietro G. Morasso,et al.  A computational theory of targeting movements based on force fields and topology representing networks , 1997, Neurocomputing.

[19]  Rodney A. Brooks,et al.  A Robust Layered Control Syste For A Mobile Robot , 2022 .

[20]  R. A. Brooks,et al.  Intelligence without Representation , 1991, Artif. Intell..

[21]  Marcello Frixione,et al.  Dynamic diagrammatic representations for reasoning and motion control , 1998, Proceedings of the 1998 IEEE International Symposium on Intelligent Control (ISIC) held jointly with IEEE International Symposium on Computational Intelligence in Robotics and Automation (CIRA) Intell.

[22]  Richard Fikes,et al.  STRIPS: A New Approach to the Application of Theorem Proving to Problem Solving , 1971, IJCAI.

[23]  Kurt Konolige,et al.  Reasoning with Analogical Representations , 1992, KR.

[24]  Kenneth D. Forbus,et al.  Qualitative Spatial Reasoning: The Clock Project , 1991, Artif. Intell..

[25]  Michael A. Arbib,et al.  Perceptual Structures and Distributed Motor Control , 1981 .

[26]  Zenon Kulpa,et al.  DIAGRAMMATIC REPRESENTATION AND REASONING , 1994 .

[27]  Gianni Vercelli,et al.  Integration of active localization systems in vehicles control for real-time trajectory tracking , 1996, 1996 IEEE/SICE/RSJ International Conference on Multisensor Fusion and Integration for Intelligent Systems (Cat. No.96TH8242).

[28]  Gianni Vercelli,et al.  A reactive sensor-based system for solving navigation problems of an autonomous robot , 1997, Proceedings of the 1997 IEEE/RSJ International Conference on Intelligent Robot and Systems. Innovative Robotics for Real-World Applications. IROS '97.

[29]  Yoram Koren,et al.  Potential field methods and their inherent limitations for mobile robot navigation , 1991, Proceedings. 1991 IEEE International Conference on Robotics and Automation.

[30]  Bernard Meltzer,et al.  Analogical Representations of Naive Physics , 1989, Artif. Intell..

[31]  Robert B. Tilove,et al.  Local obstacle avoidance for mobile robots based on the method of artificial potentials , 1990, Proceedings., IEEE International Conference on Robotics and Automation.

[32]  Daniel E. Koditschek,et al.  Exact robot navigation by means of potential functions: Some topological considerations , 1987, Proceedings. 1987 IEEE International Conference on Robotics and Automation.

[33]  Giuseppe Casalino,et al.  The Control Architecture of the Amadeus Gripper , 1998, Int. J. Syst. Sci..

[34]  Kenneth D. Forbus.  Qualitative Spatial Reasoning: Framework and Frontiers , 1992 .

[35]  N. Hari Narayanan,et al.  Reasoning with Diagrammatic Representations: A Report on the Spring Symposium , 1993, AI Mag..