foundations of computational agents
Given an informal description of a task, before even considering a computer, an agent designer should determine what would constitute a solution. This question arises not only in AI but in any software design. Much of software engineering involves refining the specification of the task.
Tasks are typically not well specified. Not only is there usually much left unspecified, but also the unspecified parts cannot be filled in arbitrarily. For example, if a user asks a trading agent to find out all the information about resorts that may have unsanitary food practices, they do not want the agent to return all the information about all resorts, even though all of the information requested is in the result. However, if the trading agent does not have complete knowledge about the resorts, returning all of the information may be the only way for it to guarantee that all of the requested information is there. Similarly, one does not want a delivery robot, when asked to take all of the trash to the garbage can, to take everything to the garbage can, even though this may be the only way to guarantee that all of the trash has been taken. Much work in AI is motivated by commonsense reasoning; we want the computer to be able to reach commonsense conclusions about the unstated assumptions.
Given a well-defined task, the next issue is whether it matters if the answer returned is incorrect or incomplete. For example, if the specification asks for all instances, does it matter if some are missing? Does it matter if there are some extra instances? Often a person does not want just any solution but the best solution according to some criteria. There are four common classes of solutions:
An optimal solution to a task is one that is the best solution according to some measure of solution quality. For example, a robot may need to take out as much trash as possible; the more trash it can take out, the better. In a more complex example, you may want the delivery robot to take as much of the trash as possible to the garbage can, minimizing the distance traveled, and explicitly specify a trade-off between the effort required and the proportion of the trash taken out. There are also costs associated with making mistakes and throwing out items that are not trash. It may be better to miss some trash than to waste too much time. One general measure of desirability, known as utility, is used in decision theory.
Often an agent does not need the best solution to a task but just needs some solution. A satisficing solution is one that is good enough according to some description of which solutions are adequate. For example, a person may tell a robot that it must take all of trash out, or tell it to take out three items of trash.
One of the advantages of a cardinal measure of success is that it allows for approximations. An approximately optimal solution is one whose measure of quality is close to the best that could theoretically be obtained. Typically, agents do not need optimal solutions to tasks; they only need to get close enough. For example, the robot may not need to travel the optimal distance to take out the trash but may only need to be within, say, of the optimal distance. Some approximation algorithms guarantee that a solution is within some range of optimal, but for some algorithms no guarantees are available.
For some tasks, it is much easier computationally to get an approximately optimal solution than to get an optimal solution. However, for other tasks, it is just as difficult to find an approximately optimal solution that is guaranteed to be within some bounds of optimal as it is to find an optimal solution.
A probable solution is one that, even though it may not actually be a solution to the task, is likely to be a solution. This is one way to approximate, in a precise manner, a satisficing solution. For example, in the case where the delivery robot could drop the trash or fail to pick it up when it attempts to, you may need the robot to be 80% sure that it has picked up three items of trash. Often you want to distinguish the false-positive error rate (the proportion of the answers given by the computer that are not correct) from the false-negative error rate (the proportion of those answers not given by the computer that are indeed correct). Some applications are much more tolerant of one of these types of errors than the other.
These categories are not exclusive. A form of learning known as probably approximately correct (PAC) learning considers probably learning an approximately correct concept (page 7.8.2).