Artificial Intelligence: Foundations of Computational Agents,  2nd Edition

Chapter 9 Planning with Uncertainty

A plan is like the scaffolding around a building. When you’re putting up the exterior shell, the scaffolding is vital. But once the shell is in place and you start to work on the interior, the scaffolding disappears. That’s how I think of planning. It has to be sufficiently thoughtful and solid to get the work up and standing straight, but it cannot take over as you toil away on the interior guts of a piece. Transforming your ideas rarely goes according to plan.

– Twyla Tharp [2003]

In the quote above, Tharp is referring to dance, but the same idea holds for any agent when there is uncertainty. An agent cannot just plan a fixed sequence of steps; the result of planning needs to be more sophisticated. Planning must take into account the fact that an agent in the real world does not know what will actually happen when it acts. An agent should plan to react to its environment.

What an agent should do at any time depends on what it will do in the future. When an agent cannot precisely predict the effects of its actions, what it will do in the future depends on what it does now and what it will observe before it acts.

With uncertainty, an agent typically cannot guarantee to satisfy its goals, and even trying to maximize the probability of achieving a goal may not be sensible. For example, an agent whose goal is to minimize the probability of injury in a car accident would not get into a car or walk down a sidewalk or even go to the ground floor of a building, each of which increases the probability of being injured in a car accident, however slightly. An agent that does not guarantee to satisfy a goal can fail in many ways, some of which may be much worse than others.

This chapter is about how to take planning, reacting, observing, succeeding and failing into account simultaneously.

An agent’s decision on what to do depends on three things:

  • the agent’s ability. The agent has to select from the options available to it.

  • what the agent believes and observes. An agent might like to condition its action on what is true in the world, but it only has access to the world via its sensors. When an agent has to decide what to do, it only has access to what it has remembered and what it observes. Sensing the world updates an agent’s beliefs. Beliefs and observations are the only information about the world available to an agent at any time.

  • the agent’s preferences. When an agent must reason with uncertainty, it has to consider not only what is most likely to happen but also what may happen. Some possible outcomes may have much worse consequences than others. The simple notion of a “goal” considered in Chapter 6, is not adequate when reasoning under uncertainty because the designer of an agent must specify trade-offs between different outcomes. For example, if an action results in a good outcome most of the time, but sometimes results in a disastrous outcome, it must be compared with performing an alternative action that results in the good outcome less often and the disastrous outcome less often and some mediocre outcome most of the time. Decision theory specifies how to trade off the desirability of outcomes with the probabilities of those outcomes.