There are numerous definitions of what artificial intelligence is.
We end up with four possible goals:
1.Systems that think like humans (focus on reasoning and human framework)
2.Systems that think rationally (focus on reasoning and a general concept of intelligence)
3.Systems that act like humans (focus on behavior and human framework)
4.Systems that act rationally (focus on behavior and a general concept of intelligence)
What is rationality? -> "doing the right thing" - simply speaking.
For (1.): "The art of creating machines that perform functions that require intelligence when performed by humans" (Kurzweil). Involves cognitive modeling - we have to determine how humans think in a literal sense (explain the inner workings of the human mind, which requires experimental inspection or psychological testing)
For (2.): "GPS - General Problem Solver” (Newell and Simon). Deals with "right thinking" and dives into the field of log. Uses logic to represent the world and relationships between objects in it and come to conclusions about it. Problems: hard to encode informal knowledge into a formal logic system and theorem provers have limitations (if there's no solution to a given logical notation).
For (3.): Turing defined intelligent behavior as the ability to achieve human-level performance in all cognitive tasks, sufficient to fool a human person (Turing Test).Physical contact to the machine has to be avoided, because physical appearance is not relevant to exhibit intelligence. However, the "Total Turing Test" includes appearance by encompassing visual input and robotics as well.
For (4.): The rational agent - achieving one's goals given one's beliefs. Instead of focusing on humans, this approach is more general, focusing on agents (which perceive and act). More general than strict logical approach (i.e. thinking rationall).
Uninformed search (blind search) has no information about the number of steps or the path costs from the current state to the goal. They can only distinguish a goal state from a non-goal state. There is no bias to go towards the desired goal.
For search algorithms, open list usually means the set of nodes yet to be explored and closed list the set of nodes that have already been explored.
•Starts with the rootnode and explores all neighboring nodes and repeats this for every one of those (expanding the "depth" of the search tree by one in each iteration). This is realized in a FIFO queue. Thus, it does an exhaustive search until it finds a goal.
•BFS is complete (i.e. it finds the goal if one exists and the branching factor is finite).
•It is optimal (i.e. if it finds the node, it will be the shallowest in the search tree).
•Explores one path to the deepest level and then backtracks until it finds a goal state. This is realized in a LIFO queue (i.e. stack).
•DFS is complete (if the search tree is finite).
•It is not optimal (it stops at the first goal state it finds, no matter if there is another goal state that is shallower than that).
•Space: (much lower than BFS).
•Time: (Higher than BFS if there is a solution on a level smaller than the maximum depth of the tree).
•Danger of running out of memory or running indefinitely for infinite trees.
3.Depth-Limited-Search / Iterative Deepening
•To avoid that, the search depth for DFS can be either limited to a constant value, or increased iteratively over time, gradually increasing the maximum depth to which it is applied. This is repeated until finding a goal. Combines advantages of DFS and BFS.
•ID is complete.
•It is optimal (the shallowest goal state will be found first, since the level is increased by one every iteration).
•Space: (better than DFS, d is depth of shallowest goal state, instead of m, maximum depth of the whole tree).
•Searches the tree both forward from the initial state and backward from the goal and stops when they meet somewhere in the middle.
•It is complete and optimal.
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