 However, we should immediately note that collaborative problem solving systems typically deal with much less predictable domain situations and user inputs than tutorial systems, and accordingly the former place much greater emphasis on flexible dialogue handling than the latter. For example, collaborators in emergency evacuation (Ferguson and Allen 1998, 2007) need to deal with a dynamically changing domain, at the same time handling the many dialogue states that may occur, depending on the participants' shared and private beliefs, goals, plans and intentions at any given point. By contrast, in a domain such as physics tutoring (e.g., Jordan et al. 2006; Litman and Silliman 2004), the learner can be guided through a network of learning goals with authored instructions, and corresponding to those goals, finite-state dialogue models can be designed that classify student inputs at each point in a dialogue and generate a prepared response likely to be appropriate for that input.