A very misused term. Today, an agent seems to mean a stand-alone piece of AI-ish software that scours across the internet doing something "intelligent." Russell and Norvig define it as "anything that can can be viewed a perceiving its environment through sensors and acting upon that environment through effectors." Several papers I've read treat it as 'any program that operates on behalf of a human,' similar to its use in the phrase 'travel agent'. Marvin Minsky has yet another definition in the book "Society of Mind." Minsky's hypothesis is that a large number of seemingly-mindless agents can work together in a society to create an intelligent society of mind. Minsky theorizes that not only will this be the basis of computer intelligence, but it is also an explaination of how human intelligence works. Andrew Moore at Carnegie Mellon University once remarked that "The only proper use of the word 'agent' is when preceded by the words 'travel', 'secret', or 'double'."
Quite a bit, actually. In 'Computing machinery and intelligence.', Alan Turing, one of the founders of computer science, made the claim that by the year 2000, computers would be able to pass the Turing test at a reasonably sophisticated level, in particular, that the average interrogator would not be able to identify the computer correctly more than 70 per cent of the time after a five minute conversation. AI hasn't quite lived upto Turing's claims, but quite a bit of progress has been made, including:
- Deployed speech dialog systems by firms like IBM, Dragon and Lernout&Hauspie
- Financial software, which is used by banks to scan credit card transactions for unusual patterns that might signal fraud. One piece of software is estimated to save banks $500 million annually.
- Applications of expert systems/case-based reasoning: a computerized Leukemia diagnosis system did a better job checking for blood disorders than human experts.
- Machine translation for Environment Canada: software developed in the 1970s translated natural language weather forcasts between English and French. Purportedly stil in use.
This topic can be somewhat sensitive, so I'll probably tread on a few toes, please forgive me. There is no authoritative answer for this question, as it really depends on what languages you like programming in. AI programs have been written in just about every language ever created. The most common seem to be Lisp, Prolog, C/C++, recently Java, and even more recently, Python.
LISP- For many years, AI was done as research in universities and laboratories, thus fast prototyping was favored over fast execution. This is one reason why AI has favored high-level langauges such as Lisp. This tradition means that current AI Lisp programmers can draw on many resources from the community. Features of the language that are good for AI programming include: garbage collection, dynamic typing, functions as data, uniform syntax, interactive environment, and extensibility. Read Paul Graham's essay, "Beating the Averages" for a discussion of some serious advantages:
PROLOG- This language wins 'cool idea' competition. It wasn't until the 70s that people began to realize that a set of logical statements plus a general theorem prover could make up a program. Prolog combines the high-level and traditional advantages of Lisp with a built-in unifier, which is particularly useful in AI. Prolog seems to be good for problems in which logic is intimately involved, or whose solutions have a succinct logical characterization. Its major drawback (IMHO)
The short answer is: MIT, CMU, and Stanford are historically the powerhouses of AI and still are the top 3 today.
There are however, hundreds of schools all over the world with at least one or two active researchers doing interesting work in AI. What is most important in graduate school is finding an advisor who is doing something YOU are interested in. Read about what's going on in the field and then identify the the people in the field that are doing that research you find most interesting. If a professor and his students are publishing frequently, then that should be a place to consider.
Explanation: Zero sum games could be multiplayer games as long as the condition for zero sum game is satisfied
Explanation: Utility values are always same and opposite.
7. Mathematical game theory, branch of economics, views of any multi-agent environment as a game provided that the impact of each agent on the others is "significant," regardless of whether the agents are cooperative or competitive.
a) Competitive Environment
Explanation: Since in cooperative environment agents' goals are I conflicts. They compete for goal.
d) Only a and b
Explanation: Depending upon games it could be single agent (Sudoku) or multi-agent (Chess)