CS461 Fundamentals and Unguided Search (Quiz 1)

Which of the following areas does the field of Artificial Intelligence borrow concepts from?

psychology, linguistics, economics, and mathematics

This school of thought holds that systems should only be considered intelligent if they follow the same reasoning process as humans. This is known as

strong AI

single agent

the agent is the only goal-directed actor in the environment

correct

the algorithm can find a valid solution

complete

the algorithm can find every solution

optimal

the algorithm can find the 'best' solution (however 'best' is defined)

informed

the algorithm can guide or direct its search some way other than blindly generating every possible state

optimally efficient

the algorithm finds the solution at least as fast (in big-O form) as any other algorithm

static

the environment will not change while the agent is deliberating

sequential

the history of previous states is also relevant

deterministic

the next problem state depends only on the current state and the action chosen

known

the outcomes of each action (or their probabilities) are available to the agent the agent is aware of the "laws of physics" for the problem it is to solve

stochastic

the transition to the next state depends on a random element in addition to the agent's action

multi-agent

there are other agents also working in the environment

continuous

time is treated as a constantly-changing flow

discrete

time, space, or both are considered in distinct unit-sized steps

The school of thought that intelligent systems should be judged by whether they achieve correct results, even if the method used to reach those results is much different than human cognition. This is known as

weak AI

What is the difference between algorithms and heuristics?

Algorithms may require too much time or storage to be practical; heuristics often get a 'good enough' answer, and do it quickly

The Turing Test is based on whether a computer system can

act humanly enough to fool a human

nondeterministic

all possible outcomes are known, but not their probabilities

observable

all relevant aspects of the environment can be perceived

The key feature of a greedy algorithm is that it

chooses the 'best' option based on purely local information

Natural language processing--for example, determining the contextual changes in definitions or links between direct and indirect references:

is much more difficult than we might have originally expected

partially observable

it is not possible to determine all relevant information about the environmet

What are characteristics of problems well-suited to AI?

none of these is correct

episodic

only the current state, not its history, matters

competitive

other agents may interfere or prevent action by this agent