In Artificial Intelligence, especially when designing intelligent agents, the process of finding a solution to a problem can often be framed as a search through a set of possible states. The agent starts in an initial state and aims to reach a goal state by transitioning through one or more intermediate states.

There are two primary categories of search algorithms:

1. Informed Search (Heuristic Search):

• These search algorithms utilize domain-specific knowledge, often in the form of heuristics, to guide the search process.
• A heuristic function estimates the “cost” or distance from the current state to the goal state.
• Informed search algorithms are generally more efficient than uninformed search algorithms for complex problems because they prioritize exploring paths that seem more promising.
• Examples of informed search algorithms include:
  • Greedy Best-First Search: Expands the node that is closest to the goal according to the heuristic function.
  • A Search:* Combines the cost to reach the current state with the estimated cost to reach the goal, aiming to find the optimal path. It uses a function f(n)=g(n)+h(n), where g(n) is the cost to reach node n, and h(n) is the heuristic estimate from n to the goal.

2. Uninformed Search (Blind Search):

• These search algorithms do not use any domain-specific knowledge or heuristics. They explore the search space systematically.
• Uninformed search algorithms can be less efficient for large problem spaces as they may explore many irrelevant paths.
• Examples of uninformed search algorithms include:
  • Breadth-First Search (BFS): Explores all the neighbor nodes at the present depth prior to moving on to the nodes at the next depth level. It guarantees finding the shortest path in terms of the number of steps.
  • Depth-First Search (DFS): Explores as far as possible along each branch before backtracking. It can find a solution faster in some cases but may not find the shortest path.
  • Uniform Cost Search (UCS): Expands the node with the lowest path cost from the start node. It finds the least-cost path if all step costs are non-negative.

3. Adversarial Search:

• You mentioned a “serial search” where someone challenges the intermediate state. This sounds like Adversarial Search, which is used in multi-agent environments where agents have conflicting goals, such as in games.
• In adversarial search, the algorithm needs to consider the actions of the opponent and try to find a strategy that maximizes its own chances of winning while minimizing the opponent’s chances.
• These algorithms often involve game trees where nodes represent game states and edges represent moves.
• Algorithms like Minimax and Alpha-Beta Pruning are commonly used in adversarial search.
  • Minimax: A recursive algorithm used to choose the optimal move for a player, assuming the opponent will also play optimally. It assigns values to each state (e.g., win, lose, draw) and works backward from the terminal states.
  • Alpha-Beta Pruning: An optimization technique for the Minimax algorithm that reduces the number of nodes that need to be evaluated in the game tree without affecting the final result. It keeps track of the best (highest) value found so far for the maximizing player (alpha) and the best (lowest) value found so far for the minimizing player (beta). If a node’s value is outside the [α,β] window, the algorithm can prune the remaining branches.

Google’s DeepMind and Adversarial Search:

You’re correct that Google’s DeepMind has demonstrated remarkable success in adversarial search, particularly in games. Their AlphaGo and AlphaZero programs, which mastered complex games like Go, chess, and shogi, utilized deep reinforcement learning combined with powerful search algorithms (like Monte Carlo Tree Search, which has elements of both informed and uninformed search and is adapted for adversarial environments). These programs learned to evaluate game states and make strategic decisions by playing against themselves millions of times.

Examples in Games:

Many games rely heavily on adversarial search algorithms in AI opponents:

• Chess and Checkers: Traditional board games where AI players use search algorithms to explore possible moves and counter-moves.
• Go: A highly complex board game where DeepMind’s AlphaGo achieved superhuman performance using advanced search techniques.
• Video Games: Many strategy and real-time games use AI opponents that employ adversarial search to challenge human players.

In summary, AI search algorithms are fundamental to enabling intelligent agents to solve problems and make decisions in various environments, including those with opposing agents. The choice of algorithm depends on the nature of the problem, the availability of domain knowledge, and the complexity of the search space.