![[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]](data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7)
Recommended
More Related Content
What's hot
What's hot (20)
Viewers also liked
Viewers also liked (11)
Similar to Lecture1
Similar to Lecture1 (20)
More from chandsek666
Recently uploaded
Recently uploaded (20)
Lecture1
- 3. Introduction to AI and Intelligent Agents
- 7. Turing’s “Imitation Game” Interrogator B (a person) A (a machine)
- 24. Environment Types (cont.) The environment type largely determines the agent design. The real world is (of course) partially observable, stochastic, sequential, dynamic, continuous, multi-agent
- 29. Example: Simple Reflex Vacuum Agent
Editor's Notes
- not new with Turing: Descartes implicitly proposed a test for distinguishing bête and homme based on distinguishability of their verbal behaviors. Descarte’s view: Animals are automata; animal behaviors are mechanical. People, as reveled in their flexible verbal behaviors, are not mechanical. Machines can’t talk, and therefore can’t think. “ But the principal argument...which may convince us that the brutes are devoid of reason, is that...it has never yet been observed that any animal has arrived at such a degree of perfection as to make use of a true language; that is to say, as to be able to indicate to us by the voice, or by other signs, anything which could be referred to by thought alone, rather than to a mere movement of nature ...; which may be taken for the true distinction between man and brute.” — René Descartes, Letter to Henry More , 1647 “ The new problem has the advantage of drawing fairly sharp line s between the physical and intellectual capacities of a man. The question and answer method seems to be suitable for introducing almost any one of the fields of human endeavor that we wish to include.” — Alan Turing, Computing Machinery and Intelligence , 1950
- There are three phases inside the loop here: figure out how the environment has changed, figure out what is the best action, figure out how this action changes the environment. The key advantage of this architecture is that the "interpret" function identifies "equivalence classes" of percepts: many different percepts correspond to the SAME environmental situation, from the point of view of what the agent should DO. Therefore the table of rules can be much smaller than the lookup table above. It is not rational for an agent to pay attention to EVERY aspect of the environment.
- There are three phases inside the loop here: figure out how the environment has changed, figure out what is the best action, figure out how this action changes the environment. The key advantage of this architecture is that the "interpret" function identifies "equivalence classes" of percepts: many different percepts correspond to the SAME environmental situation, from the point of view of what the agent should DO. Therefore the table of rules can be much smaller than the lookup table above. It is not rational for an agent to pay attention to EVERY aspect of the environment.
- LEARNING IN INTELLIGENT AGENTS With the reflex architecture, if the table of rules prescribes the wrong action, and the agent discovers this and changes the table, it has automatically generalized from its specific experience. Generalization is a key phenomenon in learning. Generalization always requires previous "background" knowledge to direct it. All complex intelligent agents will have a lot of background knowledge preprogrammed, because they do not have the time to receive enough experience and feedback from the environment to allow them to learn to behave correctly starting from scratch. In linguistics this is called the "poverty of stimulus" argument. If you calculate how many sentences a young child hears before it starts to speak correct English, the number is too few to allow it to "guess" the grammar of English. Therefore the baby must have a so-called universal natural language grammar preprogrammed into it by its genes. This argument is controversial, but there is scientific agreement that background knowledge of some sort (often very hidden and implicit) is necessary for learning in humans and AI systems.
- GOALS AND GOAL FORMULATION Often the first step in problem-solving is to simplify the performance measure that the agent is trying to maximize. Formally, a "goal" is a set of desirable world-states. "Goal formulation" means ignoring all other aspects of the current state and the performance measure, and choosing a goal. Example: if you are in Arad (Romania) and your visa will expire tomorrow, your goal is to reach Bucharest airport.