AI

1. ARTIFICIAL INTELLIGENCE

2. UNIT II PROBLEM SOLVING METHODS
• Local Search Algorithms and Optimization Problems –
• Searching with Partial Observations
• Constraint Satisfaction Problems
• Constraint Propagation –
• Backtracking Search –
• Game Playing –
• Optimal Decisions in Games –
• Alpha-Beta Pruning-
• Stochastic Games.

3. Problem-Solving Methods in AI
Problem-solving in AI refers to methods used to find solutions to problems,
often by searching through a set of possible states. These methods typically
involve algorithms that guide AI systems toward finding the most efficient
and effective solution. Common problem-solving methods include:
• Search algorithms (like Depth-First Search, Breadth-First Search, and A*).
• Optimization techniques to improve the solution.
• Machine learning methods that adapt and learn from data.
• Constraint satisfaction for problems with specific rules that must be
satisfied.

4. Local Search Algorithms and Optimization
Problems
Local search algorithms are typically used when the problem space is large,
and you cannot afford to examine every possible state. They focus on
finding a solution by exploring the local neighborhood of a given state and
iteratively improving it. These are often used in optimization problems,
where the goal is to find the best solution according to some criteria.
Common local search algorithms include:
• Hill Climbing: Moves towards the direction of increasing value.
• Simulated Annealing: Allows some random moves to avoid local maxima.
• Genetic Algorithms: Uses crossover, mutation, and selection to evolve
better solutions.

5. Searching with Partial Observations
• When the agent does not have complete information about the
environment (i.e., it can't fully observe the state), this leads to
partially observable environments. In such cases, the agent needs to
make decisions based on incomplete information.
• Algorithms like Partially Observable Markov Decision Processes
(POMDPs) are used to model and plan actions in these environments,
allowing agents to make reasonable decisions even with uncertainty.

6. Constraint Satisfaction Problems (CSP)
• A Constraint Satisfaction Problem (CSP) is a problem where the goal is to find a solution
that satisfies a set of constraints. The problem consists of variables, domains (possible
values for each variable), and constraints that restrict the values the variables can take.
• Examples of CSPs include:
• Sudoku puzzles
• Map coloring
• Scheduling problems
Common techniques for solving CSPs include:
• Backtracking (an exhaustive search method)
• Constraint propagation (eliminating impossible values early)
• Heuristic search.

7. Constraint Propagation
• Constraint propagation is a technique used in CSPs to reduce the
search space. It systematically reduces the possible values that
variables can take by applying the constraints. This is done by
eliminating inconsistent values early in the process, making the search
more efficient.
• A well-known example of constraint propagation is the Arc-
Consistency algorithm (AC-3), which ensures that each variable in the
CSP has a value that satisfies its constraints with other variables.

8. Backtracking Search
• Backtracking search is a depth-first search method used to solve CSPs.
It starts with an empty assignment of values to variables and tries to
assign values to each variable one at a time, checking if the current
assignment satisfies the constraints. If a constraint is violated, it
backtracks by undoing the most recent assignment and tries a
different value. This process continues until a solution is found or all
possibilities are exhausted.

9. Game Playing
• Game playing in AI refers to the process of developing agents that can
play games, usually involving decision-making and strategic planning.
These agents often use search algorithms to explore the possible
outcomes of moves and select the best strategy.
• Common techniques for game playing include:
• Minimax algorithm: A decision rule for minimizing the possible loss
for a worst-case scenario.
• Alpha-Beta pruning: An optimization technique for the minimax
algorithm to reduce the number of nodes that are evaluated.

10. Optimal Decisions in Games
• Optimal decision-making in games refers to choosing the best strategy
that maximizes the player's chance of winning, given the opponent's
possible responses. This is typically done by using a decision tree or a
game tree where each node represents a possible state of the game
and each edge represents a move.
• The Minimax algorithm explores this game tree and computes the
value of each move, assuming both players play optimally.

11. Alpha-Beta Pruning
• Alpha-Beta pruning is an optimization technique for the minimax
algorithm that reduces the number of nodes evaluated in the search
tree. It does this by "pruning" branches that don't need to be explored
because they cannot influence the final decision.
• Alpha is the best value found so far for the maximizing player.
• Beta is the best value found so far for the minimizing player.
• If at any point, the value of a node is worse than the current alpha or
beta value, the search is stopped at that node (pruning).
• This greatly speeds up the decision-making process by avoiding the
evaluation of branches that won't be selected.

12. Stochastic Games
• A stochastic game is a game where the outcome of moves is partially
determined by random factors or chance. This introduces uncertainty
into the decision-making process, making it different from
deterministic games like chess or checkers.
• In stochastic games, players typically need to plan for various possible
scenarios and often use Markov Decision Processes (MDPs) or
Reinforcement Learning (RL) to model the randomness and make
decisions based on probabilities.