Goal state

goal state a state of a search that represents or satisfies the goal.
GPS General Problem Solver, an early program for state space search in several application domains.

Because F is the goal state, we finish this episode. Our agent's brain now contain updated matrix Q as
If our agent learns more and more experience through many episodes, it will finally reach convergence values of Q matrix as ...

A planning problem consists of a planning domain together with an initial state of the world, and a desired goal state (or set of goal states) of the world.

Knowledge Representation and Reasoning: In a reasoning problem, one has to reach a pre-defined goal state from one or more given initial states.

Belief-Desire-Intention Model: In addition to a classical (non-BDI) agent model, the platform realizes the BDI software model, where beliefs are managed by beliefsets encapsulated within agents, desires are the goal states an agent is aspiring to ...

"If you can, always try to reduce differences between your current state, and your goal state") to solve a wide variety of problems. The rules, most of which were heuristic in nature (i.e.

Successive configurations or states of an instance are considered, with the goal of finding a goal state with a desired property.

Brute force algorithms are the most common form of searches that only require a starting state, a state description, a set of legal operators and the goal state (Korf, 1996).

For example, it is clear that planning involves the pursuit of a goal state, and goals are indeed a prominent feature of many theories applicable to planning.

If a problem is to be solved repeatedly with the same goal state but different intial state then one would like an algorithm that improves its performance over time. Learning Real-Time A* (LRTA*) is such an algorithm.

State space, unlike FSM, requires both an initial state and a goal state, and is typically used in problem solving domains where a sequence of actions is required for solving the overall problem (sequence from initial to goal states).

Breadth-first search finds the shallowest goal state, but this may not always be the least-cost solution for a general path cost function.

Formally, DFS is an uninformed search that progresses by expanding the first child node of the search tree that appears and thus going deeper and deeper until a goal state is found, or it hits a node that has no children.

See also: Artificial intelligence, Search algorithm, AI, Knowledge, Agent