4. Systems that act rationally
An agent is something that acts. To distinguish an agent from any other program it is intended to perceive its environment, adapt to
change, and operate autonomously. A rational agent is one that acts to achieve the best outcome, or best expected outcome when there
is uncertainty. Unlike the ―laws of thought‖ approach, these agents might act on incomplete knowledge or to still act when it is not
possible to prove what the correct thing to do is. This approach makes it more general than the ―laws of thought‖ approach and more
amenable to scientific development than the pure ―human-based‖ approach.
“A rational agents is one that acts so as to achieve the best outcome or, when there is uncertainty, the best expected outecome”
Agent-based activity has focused on the issues of:
1) Autonomy. Agents should be independent and communicate with others as necessary.
2) Situated. Agents should be sensitive to their own surroundings and context.
3) Interactional. Often an interface with not only humans, but also with other agents.
4) Structured. Agents cooperate in a structured society.
5) Emergent. Collection of agents more powerful than an individual agent.
Another way to think about the field of AI is in term of task domains:
Natural Language Processing, Generation, Understanding
Board Game-Playing, chess, checkers, gobblet
Verification, Theorem Proving
Design, engineering, graphics
Classification of AI is Weak vs. Strong AI:
This is essentially the human vs. non-human approach.
1) Weak AI. The study and design of machines that perform intelligent tasks. Not concerned with how tasks are performed, mostly
concerned with performance and efficiency, such as solutions that are reasonable for NP-Complete problems. E.g., to make a flying
machine, use logic and physics, don’t mimic a bird.
2) Strong AI. The study and design of machines that simulate the human mind to perform intelligent tasks. Borrow many ideas from
psychology, neuroscience. Goal is to perform tasks the way a human might do them – which makes sense, since we do have models of
human thought and problem solving. Includes psychological ideas in STM, LTM, forgetting, language, genetics, etc. Assumes that the
physical symbol hypothesis holds.
3) Evolutionary AI. The study and design of machines that simulate simple creatures, and attempt to evolve and have
higher level emergent behavior. For example, ants, bees, etc.
By: DEVESH PANDEY