Goals of Autonomous Rational Agents: Artificial intelligence

One possible way to improve an agent's performance is to enable it to have some kind of details about what it is trying to attain. If it is given some representation of its goal (for example some information about the solution to a problem that it is trying to solve), then it can be refer to that information to see if a specific action will lead to that goal. This kind of agents is called goal-based. 2 trusted and tried methods for goal-based agents are preparing (where the agent puts together and executes a plan for achieving its goal) and search (where the agent looks ahead in a search space until it finds the goal).  Searching and Planning methods are covered later in the course.

There were 2 goals in RHINO first obtain the robot to an exhibit chosen by the visitors and, when it gets there, provide information about the exhibit. Clearly RHINO used information about its goal of getting to an exhibit to plan its route to that exhibit.

Utility Functions

A goal based agent for playing chess is infeasible: every time it decides which next move to play, it sees whether that move will ultimately lead to a checkmate. Instead of this, it would be good for the agent to assess its progress not against the overall goal, but it is against a localized measure. Agent's programs often have a value function which calculates a numerical value for each world state the agent would find it in if it undertook a specific action. Then it may check which action would lead to the highest value being returned from the set of actions it has available.  As this is the rational thing to do usually the best action with respect to a utility function is taken. When the task of the agent is to find something by finding, if it uses a utility function in this manner, this is called a best-first search.

RHINO searched for paths from its present location to an exhibit, via the distance from the exhibit as a utility function. However, this was complex by visitors getting in the way.