Artificial
Intelligence

1.4.3 Reasoning and Acting

The manipulation of symbols to produce action is called reasoning.

One way that AI representations differ from computer programs in traditional languages is that an AI representation typically specifies what needs to be computed, not how it is to be computed. We might specify that the agent should find the most likely disease a patient has, or specify that a robot should get coffee, but not give detailed instructions on how to do these things. Much AI reasoning involves searching through the space of possibilities to determine how to complete a task.

In deciding what an agent will do, there are three aspects of computation that must be distinguished: (1) the computation that goes into the design of the agent, (2) the computation that the agent can do before it observes the world and needs to act, and (3) the computation that is done by the agent as it is acting.

• Design time reasoning is the reasoning that is carried out to design the agent. It is carried out by the designer of the agent, not the agent itself.
• Offline computation is the computation done by the agent before it has to act. It can include compilation and learning. Offline, the agent takes background knowledge and data and compiles them into a usable form called a knowledge base. Background knowledge can be given either at design time or offline.
• Online computation is the computation done by the agent between observing the environment and acting in the environment. A piece of information obtained online is called an observation. An agent typically must use both its knowledge base and its observations to determine what to do.

It is important to distinguish between the knowledge in the mind of the designer and the knowledge in the mind of the agent. Consider the extreme cases:

• At one extreme is a highly specialized agent that works well in the environment for which it was designed, but it is helpless outside of this niche. The designer may have done considerable work in building the agent, but the agent may not need to do very much to operate well. An example is a thermostat. It may be difficult to design a thermostat so that it turns on and off at exactly the right temperatures, but the thermostat itself does not have to do much computation. Another example is a painting robot that always paints the same parts in an automobile factory. There may be much design time or offline computation to get it to work perfectly, but the painting robot can paint parts with little online computation; it senses that there is a part in position, but then it carries out its predefined actions. These very specialized agents do not adapt well to different environments or to changing goals. The painting robot would not notice if a different sort of part were present and, even if it did, it would not know what to do with it. It would have to be redesigned or reprogrammed to paint different parts or to change into a sanding machine or a dog washing machine.
• At the other extreme is a very flexible agent that can survive in arbitrary environments and accept new tasks at run time. Simple biological agents such as insects can adapt to complex changing environments, but they cannot carry out arbitrary tasks. Designing an agent that can adapt to complex environments and changing goals is a major challenge. The agent will know much more about the particulars of a situation than the designer. Even biology has not produced many such agents. Humans may be the only extant example, but even humans need time to adapt to new environments.

Even if the flexible agent is our ultimate dream, researchers have to reach this goal via more mundane goals. Rather than building a universal agent, which can adapt to any environment and solve any task, they have built particular agents for particular environmental niches. The designer can exploit the structure of the particular niche and the agent does not have to reason about other possibilities.

Two broad strategies have been pursued in building agents:

• The first is to simplify environments and build complex reasoning systems for these simple environments. For example, factory robots can do sophisticated tasks in the engineered environment of a factory, but they may be hopeless in a natural environment. Much of the complexity of the problem can be reduced by simplifying the environment. This is also important for building practical systems because many environments can be engineered to make them simpler for agents.
• The second strategy is to build simple agents in natural environments. This is inspired by seeing how insects can survive in complex environments even though they have very limited reasoning abilities. Researchers then make the agents have more reasoning abilities as their tasks become more complicated.

One of the advantages of simplifying environments is that it may enable us to prove properties of agents or to optimize agents for particular situations. Proving properties or optimization typically requires a model of the agent and its environment. The agent may do a little or a lot of reasoning, but an observer or designer of the agent may be able to reason about the agent and the environment. For example, the designer may be able to prove whether the agent can achieve a goal, whether it can avoid getting into situations that may be bad for the agent (safety goals), whether it will get stuck somewhere (liveness), or whether it will eventually get around to each of the things it should do (fairness). Of course, the proof is only as good as the model.

The advantage of building agents for complex environments is that these are the types of environments in which humans live and want our agents to live.

Fortunately, research along both lines is being carried out. In the first case, researchers start with simple environments and make the environments more complex. In the second case, researchers increase the complexity of the behaviors that the agents can carry out.