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Goal Based Agents - Pathfinding - Coggle Diagram
Goal Based Agents - Pathfinding
Definition of Goal Based Agents
Goal-based agents are intelligent systems that utilize goal-oriented data to make decisions, advancing beyond simple model-based agents.
Path Finding
Pathfinding involves finding the quickest or most effective path from a given starting point to a desired destination.
Types of Searching Algorithms
DFS (Depth-First Search)
Uses a stack data structure for implementation
May not necessarily find the shortest path.
Explores as far as possible along each branch before backtracking.
Requires less memory compared to BFS
DFS may not find a solution if the graph has cycles
BFS(Breadth-First Search)
Requires more memory compared to DFS
Uses a queue data structure for implementation.
Guarantees the shortest path in unweighted graphs
BFS is complete, meaning it will find a solution if one exists, provided the graph is finite.
Explores all neighbors of a node before moving on to the next level.
A* Search
Considers both the cost to reach a node and the estimated cost to reach the goal.
Uses a priority queue based on the total estimated cost.
Heuristic function guides the search towards the most promising nodes.
Completeness and optimality depend on the heuristic function.
Well Defined Problem
Initial State:
The starting condition or position of the problem.
Possible Actions:
All actions or moves that can be taken from any state.
Transition Model:
Rules that define how actions change the current state to a new state.
Web Links
https://www.javatpoint.com/types-of-ai-agents
https://www.spiceworks.com/tech/tech-general/articles/what-is-fsm/
https://www.javatpoint.com/ai-uninformed-search-algorithms
Digraph
Directed graph (digraph) involves edges with direction, indicating one-way relationships between vertices.
Represent entities or points within the graph.
Directed arrows denote relationships between vertices, specifying influence or direction.
Directed edges show relationships between vertices, emphasizing direction.
Network Edges
nodes
The nodes represent different entities
Edges
Edges convey information about the links between the nodes
Types of Network Edges
Weighted edges
Undirected edges
Directed edges
Problem Solving Steps
Search
Looks for sequence of actions to reach goal.
Goal Formulation
Based on current situation and performance measure.
Space State
A state space serves as a mathematical representation of a problem, encompassing all conceivable states in which the problem may exist.
Representing state space entails establishing both an initial state and a goal state, followed by identifying a series of actions, referred to as states, to transition from the initial to the goal state.