TY - GEN
T1 - Relational preference rules for control
AU - Brafman, Ronen I.
N1 - Funding Information:
I am grateful to Manfred Jaeger for his help relating this work to PRMs, to Liron Himi, Ofer Schonberger, Yuval Shahar, and Giora Shcherbakov, for system development, to Yagil Engel for comments on earlier drafts. This work was supported in part by ISF grant 1101/07, the Paul Ivanier Center for Robotics Research and Production Management, the Lynn and William Frankel Center for Computer Science, and COST action IC0602.
PY - 2008/1/1
Y1 - 2008/1/1
N2 - Much like relational probabilistic models, the need for relational preference models arises naturally in real-world applications where the set of object classes is fixed, but object instances vary from one application to another as well as within the run-time of a single application. To address this problem, we suggest a rule-based preference specification language. This language extends regular rule-based languages and leads to a much more flexible approach for specifying control rules for autonomous systems. It also extends standard generalized-additive value functions to handle a dynamic universe of objects: given any specific set of objects it induces a generalized-additive value function. Throughout the paper we use the example of a decision support system for command and control centers we are currently developing to motivate the need for such models and to illustrate them.
AB - Much like relational probabilistic models, the need for relational preference models arises naturally in real-world applications where the set of object classes is fixed, but object instances vary from one application to another as well as within the run-time of a single application. To address this problem, we suggest a rule-based preference specification language. This language extends regular rule-based languages and leads to a much more flexible approach for specifying control rules for autonomous systems. It also extends standard generalized-additive value functions to handle a dynamic universe of objects: given any specific set of objects it induces a generalized-additive value function. Throughout the paper we use the example of a decision support system for command and control centers we are currently developing to motivate the need for such models and to illustrate them.
UR - https://www.scopus.com/pages/publications/79954847834
M3 - Conference contribution
AN - SCOPUS:79954847834
SN - 9781577353843
T3 - Proceedings of the International Conference on Knowledge Representation and Reasoning
SP - 552
EP - 559
BT - Principles of Knowledge Representation and Reasoning
PB - Institute of Electrical and Electronics Engineers
T2 - 11th International Conference on Principles of Knowledge Representation and Reasoning, KR 2008
Y2 - 16 September 2008 through 19 September 2008
ER -