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Results
Symbolic and neural learning algorithms: An experimental comparison
Shavlik, J. W. | Mooney, R. | Towell, G. G.
Machine Learning, 6(1): 111-143.
O-Plan: the Open Planning Architecture,
Artificial Intelligence vol. 52, pp 49-86, 1991.
Interactions between learning and evolution
Ackley, D. H. | Littman, M. L.
In Langton, C., Taylor, C., Farmer, J. D., and Ramussen, S. (Eds.), Artificial Life II, pp. 487–509. Addison-Wesley
Systematic nonlinear planning
McAllester, D. A. | Rosenblitt, D.
The paper Systematic Nonlinear Planning (published in the Proceedings of the Ninth National Conference on Artificial Intelligence (AAAI-91) won the AAAI-10 classic paper award. The commentary by Daniel S. Weld describes the two major impacts the paper had on the field of automated planning.See also:Commentary by Dan WeldAn Introduction to Least Commitment Planning, Daniel S. Weld, AI Magazine, Vol 15, No 4 Proc. AAAI-91, Vol. 2, pp. 634–639
A Robot Exploration and Mapping Strategy
At the control level, distinctive places and distinctive travel edges are identified based on the interaction between the robot's control strategies, its sensorimotor system, and the world. A distinctive place is defined as the local maximum of a distinctiveness measure appropriate to its immediate neighborhood, and is found by a hill-climbing control strategy. A distinctive travel edge, similarly, is defined by a suitable measure and a path-following control strategy. The topological network description is created by linking the distinctive places and travel edges. Metrical information is then incrementally assimilated into local geometric descriptions of places and edges, and finally merged into a global geometric map.
The vector field histogram — Fast obstacle avoidance for mobile robots
IEEE Transactions on Robotics and Automation, 7 (3), 278-88
Optimal probabilistic and decision-theoretic planning using Markovian decision theory
Masterâs report, Computer Science Division, University of California.
Experiments with Proof Plans for Induction
Bundy, A. | Harmelen, F. | Hesketh, J. | Smaill, A.
Abstraction, in contrast to meta-level inference, works with a degenerate version of the object-level space in which some essential detail is thrown away. Because abstract plans are strongly tied to the object-level space, they are limited in their expressive power.