(Schreckenghost, 1999). A similar area where AA technology is required is for safety-critical intelligent software, such as for controlling nuclear power plants and oil refineries (Musliner
& Krebsbach, 1999). That work has resulted in a system called AEGIS (Abnormal Event Guidance and Information System) that combines human and agent capabilities for rapid reaction to emergencies in a petro-chemical refining plant. AEGIS features a shared task representation that both the users and the intelligent system can work with (Goldman, Guerlain, Miller, & Musliner, 1997). A key hypothesis of the work is that the model needs to have multiple levels of abstraction so that the user can interact at the level they see fit.

Interesting work by Fong, Thorpe, and Baur (2002) has extended the idea of tele-operated robotics by re-defining the relationship between the robot and user as a collaborative one, rather than the traditional master-slave configuration. In particular, the robot treats the human as a resource that can perform perceptual or cognitive functions that the robot determines it cannot adequately perform. However, as yet the work has not looked at the possibility that the user is not available to provide input when required, which would require the robot perform more complex transfer-of-control reasoning.

While most previous work in AA has ignored complex strategies for AA, there is work in other research fields that is potentially relevant. For example, the research issues addressed by fields such as mixed-initiative decision-making (Collins, Bilot, Gini, & Mobasher, 2000b), anytime algorithms (Zilberstein, 1996), multi-processor scheduling (Stankovic, Ramamritham, & Cheng, 1985), meta-reasoning (Russell & Wefald, 1989), game theory (Fudenberg & Tirole, 1991), and contingency plans (Draper, Hanks, & Weld, 1994; Peot & Smith, 1992) all have, at least superficial, similarities with the AA problem. However, it turns out that the core assumptions and focus of these other research areas are different enough that the algorithms developed in these related fields are not directly applicable to the AA problem.

In mixed-initiative decision making a human user is assumed to be continually available
(Collins et al., 2000; Ferguson & Allen, 1998), negating any need for reasoning about the likelihood of response. Furthermore, there is often little or no time pressure or coordination constraints. Thus, while the basic problem of transferring control between a human and agent is common to both mixed-initiative decision making and AA, the assumptions are quite different leading to distinct solutions. Likewise, other related research fields make distinctly different assumptions which lead to distinctly different solutions. For instance, contingency planning (Draper et al., 1994; Peot & Smith, 1992) deals with the problem of creating plans to deal with critical developments in the environment. Strategies are related to contingency planning in that they are plans to deal with the specific contingency of an entity not making a decision in a manner that maintains coordination. However, in contingency planning, the key difficulty is in creating the plans. In contrast, in AA, creating strategies is straightforward and the key difficulty is choosing between those strategies.  Our contribution is in recognizing the need for strategies in addressing the AA problem, instantiating such strategies via MDPs, and the development of a general, domain-independent reward function that leads to an MDP choosing the optimal strategy for a particular situation.

Similarly, another related research area is meta-reasoning (Russell & Wefald, 1989).

Meta-reasoning work looks at online reasoning about computation. A type of meta-reasoning, most closely related to AA, chooses between sequences of computations with different ex¬