198 S Schiaffino and A. Amandi hence, the user's goal. Similarly, the Lumiere project at Microsoft Research( Hor- vitz et al., 1998)uses Bayesian networks to infer a users needs by considering a user's background, actions and queries(help requests). Based on the beliefs of a user's needs and the utility theory of influence diagrams(an extension to Bayesian networks), an automated assistant provides help for users. In Andes( gertner and VanLehn, 2000), plan recognition is necessary for the problem solving coach select what step to suggest when a student asks for help. Since Andes wants to help students solve problems in their own way, it must determine what goal the student is probably trying to achieve, and suggest the action the student cannot perform due to lack of knowledge 2.4 Behaviour Usually, the user's behaviour with a software application is an important part of the user profile. If a given user behaviour is repetitive, then it represents a pattern that can be used by an adaptive system or an intelligent agent to adapt a web site or to assist the user according to the behaviour learnt. The type of behaviour mod- elled depends on the application domain. For example, CAP (Calendar APpren- tice)learns the scheduling behaviour of its user and learns rules that enable it to suggest the meeting duration, location, time, and date(Mitchell et al, 1994 ) In an intelligent e-commerce system, a behavioural profile models the customer's ac tions(Adomavicius and Tuzhilin, 2001). Examples of behaviours in this domain e"When purchasing cereal, John Doe usually buys milk"and"On weekend John Doe usually spends more than $100 on groceries". In intelligent tutoring systems, the student behaviour is vital to assist him properly. In(Xu, 2002),a student profile is a set of <t, e> pairs, where e is a behaviour of the student and t expresses the time when the behaviour occurs. t could be a point in time or interval of time. In this work, there are two main types of student behaviours and making a choice in a quiz. ometimes behaviours are routine, that is, they show some kind of regularity or seasonality. For example, Query Guesser(Schiaffino and Amandi, 2005) models a users routine queries to a database in a Laboratory Information Management System. In this agent, the user profile is composed of the queries each user performs F如25.9 Fae18.39 plectcontact ComposeMailTod MIrs. 333 Fig. 2. Bayesian representation of a user's goal198 S. Schiaffino and A. Amandi hence, the user’s goal. Similarly, the Lumiere project at Microsoft Research (Hor￾vitz et al., 1998) uses Bayesian networks to infer a user’s needs by considering a user’s background, actions and queries (help requests). Based on the beliefs of a user’s needs and the utility theory of influence diagrams (an extension to Bayesian networks), an automated assistant provides help for users. In Andes (Gertner and VanLehn, 2000), plan recognition is necessary for the problem solving coach to select what step to suggest when a student asks for help. Since Andes wants to help students solve problems in their own way, it must determine what goal the student is probably trying to achieve, and suggest the action the student cannot perform due to lack of knowledge. 2.4 Behaviour Usually, the user’s behaviour with a software application is an important part of the user profile. If a given user behaviour is repetitive, then it represents a pattern that can be used by an adaptive system or an intelligent agent to adapt a web site or to assist the user according to the behaviour learnt. The type of behaviour mod￾elled depends on the application domain. For example, CAP (Calendar APpren￾tice) learns the scheduling behaviour of its user and learns rules that enable it to suggest the meeting duration, location, time, and date (Mitchell et al, 1994). In an intelligent e-commerce system, a behavioural profile models the customer’s ac￾tions (Adomavicius and Tuzhilin, 2001). Examples of behaviours in this domain are “When purchasing cereal, John Doe usually buys milk” and “On weekends, John Doe usually spends more than $100 on groceries”. In intelligent tutoring systems, the student behaviour is vital to assist him properly. In (Xu, 2002), a student profile is a set of <t, e> pairs, where e is a behaviour of the student and t expresses the time when the behaviour occurs. t could be a point in time or an interval of time. In this work, there are two main types of student behaviours, reading a particular topic and making a choice in a quiz. Sometimes behaviours are routine, that is, they show some kind of regularity or seasonality. For example, QueryGuesser (Schiaffino and Amandi, 2005) models a user’s routine queries to a database in a Laboratory Information Management System. In this agent, the user profile is composed of the queries each user performs Fig. 2. Bayesian representation of a user’s goals