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S. Russell and E. Wefald (1991). Do the right thing : studies in limited rationality (Chapter 1: Limited Rationality), MIT Press

  author =       "Stuart Russell and Eric Wefald",
  title =        "Do the right thing : studies in limited rationality",
  publisher =    "MIT Press",
  year =         "1991",
  series =       "Artificial intelligence",
  address =      "Cambridge, Mass",

Author of the summary: David Furcy, 1999, dfurcy@cc.gatech.edu

Cite this paper for:


This chapter defines bounded optimal agents as agents that act optimally given their limited computational resources. The authors propose to redefine the whole AI discipline as the design of such agents, as opposed to the traditional logicist and decision-theoretic approaches that do not take resource limitations into account. "The keystone of [the former] approach is the ability of reasoning systems to reason about their own deliberations." Using an array of representational schemes (from declarative to production-like) as well as knowledge compilation methods, a metareasoning architecture should aim at achieving bounded rationality as "an equilibrium point of the forces that tend to maximize the quality of decisions, minimize the time to make those decisions, and maximize the speed of adaptation to the environment."

Detailed outline


An agent is a system that senses its environment and acts upon it. An agent can be described by a mapping from percept sequences to actions. It is important to distinguish: The program implements the mapping. When run on an architecture (a fixed interpreter for a class of programs), it will create the agent's behavior.

AI as designing agents that "do the right thing"

What does it mean to do the right thing? For logicists, it means deriving from the agent's beliefs an action sequence that will provably achieve its goals. For decision-theorists, it means finding an action sequence that will maximize expected payoff or utility.

The main problem with these prescriptive approaches is that they judge "rightness" only on the product of reasoning (namely the action sequence) as opposed to evaluating the whole "deliberating plus acting" process. In other words, they focus on the abstract mapping, not on the behavior. But a program that implements the optimal mapping may be completely useless in practice if it takes forever to run.

An alternative approach is to judge "rightness" based on the behavior of the agent, not the abstract mapping. This implies taking into account running the program on a given architecture and therefore leads to the consideration of the available, limited resources. In a way, they favor "acting rationally" over "thinking rationally."

Different types of rationality

  1. Perfect or "Type I" rationality
    This is the decision-theoretic principle that an agent should always act so as to maximize its expected utility. Not realistic for complex environments.
  2. Good's "Type II" rationality
    Same as before while taking into account deliberation costs. Requires perfect management of deliberations and still seems unrealistic.
  3. Bounded optimality
    This means acting optimally taking into account the agent's limited computational resources. AI = Designing bounded rational agents, i.e. maximizing the agent's utility over all the behaviors enabled by the architecture. Such an agent must be able to trade off action quality against urgency, in particular through metareasoning.

Constraints on the design of bounded optimal agents

In conclusion, the architecture should allow for different types of knowledge and the agent's program should contain compilation mechanisms from declarative to production-like knowledge structures. (Cf. chapter 2)


Computations are actions with utilities or expected values. The latter depend on the effects in terms of the passage of time and the difference between the external actions they lead to and those that were favored before the deliberation. Metareasoning is the problem of deciding whether or not to deliberate (at the object level), of deciding what to think about and of allocating resources for deliberation.

The authors argue that metareasoning is domain-independent. It does not need to know what the object level decisions are about. It need only compute what their expected utility is. The key to metareasoning is to take advantage of structural regularity in the domain, namely that not all computations will have the same expected utility. This non-uniformity must be learnable/predictable.

Summary author's notes:

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