%0 Conference Paper %1 ChiuWebbZheng97 %A Chiu, B. C. %A Webb, G. I. %A Zheng, Z. %B Lecture Notes in Computer Science Vol. 1342: Proceedings of the Tenth Australian Joint Conference on Artificial Intelligence (AI'97) %C Berlin %D 1997 %E Sattar, A. %I Springer-Verlag %K Based Feature Modeling Modeling, User %P 349-358 %T Using Decision Trees for Agent Modelling: A Study on Resolving Conflicting Predictions %X Input-Output Agent Modelling (IOAM) is an approach to modelling an agent in terms of relationships between the inputs and outputs of the cognitive system. This approach, together with a leading inductive learning algorithm, C4.5, has been adopted to build a subtraction skill modeller, C4.5-IOAM. It models agents' competencies with a set of decision trees. C4.5-IOAM makes no prediction when predictions from different decision trees are contradictory. This paper proposes three techniques for resolving such situations. Two techniques involve selecting the more reliable prediction from a set of competing predictions using a free quality measure and a leaf quality measure. The other technique merges multiple decision trees into a single tree. This has the additional advantage of producing more comprehensible models. Experimental results, in the domain of modelling elementary subtraction skills, showed that the tree quality and the leaf quality of a decision path provided valuable references for resolving contradicting predictions and a single tree model representation performed nearly equally well to the multi-tree model representation.

@inproceedings{ChiuWebbZheng97, abstract = {Input-Output Agent Modelling (IOAM) is an approach to modelling an agent in terms of relationships between the inputs and outputs of the cognitive system. This approach, together with a leading inductive learning algorithm, C4.5, has been adopted to build a subtraction skill modeller, C4.5-IOAM. It models agents' competencies with a set of decision trees. C4.5-IOAM makes no prediction when predictions from different decision trees are contradictory. This paper proposes three techniques for resolving such situations. Two techniques involve selecting the more reliable prediction from a set of competing predictions using a free quality measure and a leaf quality measure. The other technique merges multiple decision trees into a single tree. This has the additional advantage of producing more comprehensible models. Experimental results, in the domain of modelling elementary subtraction skills, showed that the tree quality and the leaf quality of a decision path provided valuable references for resolving contradicting predictions and a single tree model representation performed nearly equally well to the multi-tree model representation.}, added-at = {2016-03-20T05:42:04.000+0100}, address = {Berlin}, audit-trail = {Reconstructed paper posted 11/10/05}, author = {Chiu, B. C. and Webb, G. I. and Zheng, Z.}, biburl = {https://www.bibsonomy.org/bibtex/2c017132467f4e23e59a3d717a0f823d5/giwebb}, booktitle = {Lecture Notes in Computer Science Vol. 1342: Proceedings of the Tenth Australian Joint Conference on Artificial Intelligence (AI'97)}, editor = {Sattar, A.}, interhash = {bf4efbd437ed680e28f7ef39c664cd6b}, intrahash = {c017132467f4e23e59a3d717a0f823d5}, keywords = {Based Feature Modeling Modeling, User}, location = {Perth, Australia}, pages = {349-358}, publisher = {Springer-Verlag}, timestamp = {2016-03-20T05:42:04.000+0100}, title = {Using Decision Trees for Agent Modelling: A Study on Resolving Conflicting Predictions}, year = 1997 }