Artificial intelligence notes
Artificial intelligence notes
The branch of computer science concerned with making computers behave like humans. The term was coined in 1956 by John McCarthy at the Massachusetts Institute of Technology. Artificial intelligence includes
Games playing: programming computers to play games such as chess and checkers
Expert systems : programming computers to make decisions in real-life situations (for example, some expert systems help doctors diagnose diseases based on symptoms)
Natural language : programming computers to understand natural human languages
Neural networks : Systems that simulate intelligence by attempting to reproduce the types of physical connections that occur in animal brains
Robotics : programming computers to see and hear and react to other sensory stimuli
Currently, no computers exhibit full artificial intelligence (that is, are able to simulate human behavior). The greatest advances have occurred in the field of games playing. The best computer chess programs are now capable of beating humans. In May, 1997, an IBM super-computer called Deep Blue defeated world chess champion Gary Kasparov in a chess match.
In the area of robotics, computers are now widely used in assembly plants, but they are capable only of very limited tasks. Robots have great difficulty identifying objects based on appearance or feel, and they still move and handle objects clumsily.
Natural-language processing offers the greatest potential rewards because it would allow people to interact with computers without needing any specialized knowledge. You could simply walk up to a computer and talk to it. Unfortunately, programming computers to understand natural languages has proved to be more difficult than originally thought. Some rudimentary translation systems that translate from one human language to another are in existence, but they are not nearly as good as human translators. There are also voice recognition systems that can convert spoken sounds into written words, but they do not understand what they are writing; they simply take dictation. Even these systems are quite limited — you must speak slowly and distinctly.
In the early 1980s, expert systems were believed to represent the future of artificial intelligence and of computers in general. To date, however, they have not lived up to expectations. Many expert systems help human experts in such fields as medicine and engineering, but they are very expensive to produce and are helpful only in special situations.
Today, the hottest area of artificial intelligence is neural networks, which are proving successful in a number of disciplines such as voice recognition and natural-language processing.
There are several programming languages that are known as AI languages because they are used almost exclusively for AI applications. The two most common are LISP and Prolog.
Types of Production System
There are two basic types of production System:
- Commutative Production System
- Decomposable Production System
Commutative Production System :-A production system is commutative if it has the following properties with respect to a database D:
1. Each member of the set of rules applicable to D is also applicable to any database produced by applying an applicable rule to D.
2. If the goal condition is satisfied by D, then it is also satisfied by any database produced by applying any applicable rule to D.
3. The database that results by applying to D any sequence composed of rules that are applicable to D is invariant under permutations of the sequence.
Decomposable Production System :-Initial database can be decomposed or split into separate components that can be processed independently.
Iterative Deepening Depth-First Search
Iterative deepening depth-first search (IDDFS) is a state space search strategy in which a depth-limited search is run repeatedly, increasing the depth limit with each iteration until it reaches d, the depth of the shallowest goal state. On each iteration, IDDFS visits the nodes in the search tree in the same order as depth-first search, but the cumulative order in which nodes are first visited, assuming no pruning, is effectively breadth-first.
IDDFS combines depth-first search’s space-efficiency and breadth-first search’s completeness (when the branching factor is finite). It is optimal when the path cost is a non-decreasing function of the depth of the node.
The space complexity of IDDFS is O(bd), where b is the branching factor and d is the depth of shallowest goal. Since iterative deepening visits states multiple times, it may seem wasteful, but it turns out to be not so costly, since in a tree most of the nodes are in the bottom level, so it does not matter much if the upper levels are visited multiple times.
The main advantage of IDDFS in game tree searching is that the earlier searches tend to improve the commonly used heuristics, such as the killer heuristic and alpha-beta pruning, so that a more accurate estimate of the score of various nodes at the final depth search can occur, and the search completes more quickly since it is done in a better order. For example, alpha-beta pruning is most efficient if it searches the best moves first.
A second advantage is the responsiveness of the algorithm. Because early iterations use small values for d, they execute extremely quickly. This allows the algorithm to supply early indications of the result almost immediately, followed by refinements as d increases. When used in an interactive setting, such as in a chess-playing program, this facility allows the program to play at any time with the current best move found in the search it has completed so far. This is not possible with a traditional depth-first search.
The time complexity of IDDFS in well-balanced trees works out to be the same as Depth-first search: O(bd).
In an iterative deepening search, the nodes on the bottom level are expanded once, those on the next to bottom level are expanded twice, and so on, up to the root of the search tree, which is expanded d + 1 times. So the total number of expansions in an iterative deepening search is
All together, an iterative deepening search from depth 1 to depth d expands only about 11% more nodes than a single breadth-first or depth-limited search to depth d, when b = 10. The higher the branching factor, the lower the overhead of repeatedly expanded states, but even when the branching factor is 2, iterative deepening search only takes about twice as long as a complete breadth-first search. This means that the time complexity of iterative deepening is still O(bd), and the space complexity is O(bd). In general, iterative deepening is the preferred search method when there is a large search space and the depth of the solution is not known.