A formal theory of plan recognition
A formal theory of plan recognition
A Bayesian model of plan recognition
Artificial Intelligence
Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Agents that reduce work and information overload
Communications of the ACM
Parallel mining algorithms for generalized association rules with classification hierarchy
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Mining generalized association rules
Future Generation Computer Systems - Special double issue on data mining
Reaching agreements through argumentation: a logical model and implementation
Artificial Intelligence
Computational Modeling of Behavior in Organizations: The Third Scientific Discipline
Computational Modeling of Behavior in Organizations: The Third Scientific Discipline
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
A Framework for Argumentation-Based Negotiation
ATAL '97 Proceedings of the 4th International Workshop on Intelligent Agents IV, Agent Theories, Architectures, and Languages
Argumentation-based negotiation
The Knowledge Engineering Review
Incorporating a user model into an information theoretic framework for argument interpretation
UM'03 Proceedings of the 9th international conference on User modeling
User models for adaptive hypermedia and adaptive educational systems
The adaptive web
Assisting students with argumentation plans when solving problems in CSCL
Computers & Education
Expert Systems with Applications: An International Journal
An adaptive approach for decision making tactics in automated negotiation
Applied Intelligence
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Knowing how a user builds his/her arguments during a discussion gives useful advantages if we want to assist the user or analyse his/her argumentative skills. This paper presents a novel mechanism to build user argumentative models, which captures the argumentative style to generate arguments. To this end, we observe how users generate arguments, and apply a generalised association rules algorithm to discover rules for argument generation. These rules depict the argumentative style of the user. They are composed of an antecedent, which represents the conditions to build an argument, and a consequent, which represents such argument. To evaluate this proposal, we show results obtained in the domain of meeting scheduling. We discovered interesting rules from a group of users discussing in that domain, and checked that about 60% of the arguments that users had generated in a test situation can be also generated from the rules previously learnt, at least partially. Finally, although this work focuses on modelling users' argumentative style, we discuss how this promising approach could be applied in different knowledge domains.