pected quality and running time, subject to the constraint that oosing the highest-quality ssseeeeeeeeeee 1989). The idea is to treat computations as actions and "meta-reason" about the EU of doing certain combinations of computation and (base-level) actions. The output of metareasoning is a sequence of computations that are executed in sequence. AA parallels metareasoning if we consider reasoning about transferring control to entities as reasoning about selecting computations, i.e., we think of entities as computations. However, in AA, the aim is to have one entity make a high-quality decision, while in meta-reasoning, the aim is for a sequence of computations to have some high quality. Moreover, the meta-reasoning assumption that computations are guaranteed to return a timely result if executed, does not apply in AA. Finally, meta-reasoning looks for a sequence of computations that use a fixed amount of time, while AA reasons about trading off extra time for a better decision (possibly buying time with a D action). Thus, algorithms developed for meta-reasoning are not applicable to AA.

Another research area with conceptual similarity to AA is the field of anytime algorithms (Zilberstein, 1996). An anytime algorithm quickly finds an initial solution and then incrementally tries to improve the solution until stopped. The AA problem is similar when we assume that the agent itself can make an immediate decision, because the problem then has the property that a solution is always available (an important property of an anytime algorithm). However, this will not be the case in general, i.e., the agent will not always have an answer. Furthermore, anytime algorithms do not generally need to deal with multiple, distributed entities, nor do they have the opportunity to change coordination (i.e., using a D action).

Multi-processor scheduling looks at assigning tasks to nodes in order to meet certain time constraints (Stankovic et al., 1985). If entities are thought of as "nodes", then AA is also about assigning tasks to nodes. In multiprocessor scheduling, the quality of the computation performed on each of the nodes is usually assumed to be equal, i.e., the nodes are homogeneous.  Thus, reasoning that trades off quality and time is not required, as it is in A. Moreover, deadlines are externally imposed for multi-processor scheduling algorithms, rather than being flexibly reasoned about as in AA. Multi-processor scheduling algorithms can sometimes deal with a node rejecting a task because it cannot fulfill the time constraints or network failures. However, while the AA problem focuses on failure to get a response as a central issue and load balancing as an auxiliary issue, multi-processor scheduling has the opposite focus.  The difference in focus leads to algorithms being developed in the multiprocessor scheduling community that are not well suited to AA (and vice versa).

## Conclusions 7.

Adjustable autonomy is critical to the success of real-world agent systems because it allows an agent to leverage the skills, resources and decision-making abilities of other entities, both human and agent. Previous work has addressed AA in the context of single-agent and single-human scenarios, but those solutions do not scale to increasingly complex multiagent systems. In particular, previous work used rigid, one-shot transfers of control that did not consider team costs and, more importantly, did not consider the possibility of costly