TY - JOUR
T1 - Task modeling with reusable problem-solving methods
AU - Eriksson, Henrik
AU - Shahar, Yuval
AU - Tu, Samson W.
AU - Puerta, Angel R.
AU - Musen, Mark A.
N1 - Funding Information:
This work has been supported in part by grants LM05157 and LM05208 from the National Library of Medicine, by grant HS06330 from the Agency for Health Care Policy and Research, by gifts from Digital Equipment Corporation, and by scholarships from the Swedish Institute, from the Fulbright Commission, and from Stanford University. Dr. Musen is recipient of National Science Foundation Young Investigator Award IRI-9257578.
PY - 1995/1/1
Y1 - 1995/1/1
N2 - Problem-solving methods for knowledge-based systems establish the behavior of such systems by defining the roles in which domain knowledge is used and the ordering of inferences. Developers can compose problem-solving methods that accomplish complex application tasks from primitive, reusable methods. The key steps in this development approach are task analysis, method selection (from a library), and method configuration. Protégé-ii is a knowledge-engineering environment that allows developers to select and configure problem-solving methods. In addition, Protégé-ii generates domain-specific knowledge-acquisition tools that domain specialists can use to create knowledge bases on which the methods may operate. The board-game method is a problem-solving method that defines control knowledge for a class of tasks that developers can model in a highly specific way. The method adopts a conceptual model of problem solving in which the solution space is construed as a "game board" on which the problem solver moves "playing pieces" according to prespecified rules. This familiar conceptual model simplifies the developer's cognitive demands when configuring the board-game method to support new application tasks. We compare configuration of the board-game method to that of a chronological-backtracking problem-solving method for the same application tasks (for example, towers of Hanoi and the Sisyphus room-assignment problem). We also examine how method designers can specialize problem-solving methods by making ontological commitments to certain classes of tasks. We exemplify this technique by specializing the chronological-backtracking method to the board-game method.
AB - Problem-solving methods for knowledge-based systems establish the behavior of such systems by defining the roles in which domain knowledge is used and the ordering of inferences. Developers can compose problem-solving methods that accomplish complex application tasks from primitive, reusable methods. The key steps in this development approach are task analysis, method selection (from a library), and method configuration. Protégé-ii is a knowledge-engineering environment that allows developers to select and configure problem-solving methods. In addition, Protégé-ii generates domain-specific knowledge-acquisition tools that domain specialists can use to create knowledge bases on which the methods may operate. The board-game method is a problem-solving method that defines control knowledge for a class of tasks that developers can model in a highly specific way. The method adopts a conceptual model of problem solving in which the solution space is construed as a "game board" on which the problem solver moves "playing pieces" according to prespecified rules. This familiar conceptual model simplifies the developer's cognitive demands when configuring the board-game method to support new application tasks. We compare configuration of the board-game method to that of a chronological-backtracking problem-solving method for the same application tasks (for example, towers of Hanoi and the Sisyphus room-assignment problem). We also examine how method designers can specialize problem-solving methods by making ontological commitments to certain classes of tasks. We exemplify this technique by specializing the chronological-backtracking method to the board-game method.
UR - http://www.scopus.com/inward/record.url?scp=0029485282&partnerID=8YFLogxK
U2 - 10.1016/0004-3702(94)00040-9
DO - 10.1016/0004-3702(94)00040-9
M3 - Article
AN - SCOPUS:0029485282
SN - 0004-3702
VL - 79
SP - 293
EP - 326
JO - Artificial Intelligence
JF - Artificial Intelligence
IS - 2
ER -