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	== Terminology ==		== Terminology ==
	* FSE - Formulate, Search, Execute.		* '''FSE''' - Formulate, Search, Execute.
	* Path - A sequence of states, connected by a sequence of actions.		* '''Path''' - A sequence of states, connected by a sequence of actions.
	* Initial State - The agent's starting state.		* '''Initial State''' - The agent's starting state.
	* Successor State - Any state reachable by a single action, from the current state.		* '''Successor State''' - Any state reachable by a single action, from the current state.
	* State Space - The set of all states reachable from the initial state, after some set of actions.		* '''State Space''' - The set of all states reachable from the initial state, after some set of actions.
	** Note: This exclude any states that are only reachable by proceeding past the goal state.		** Note: This exclude any states that are only reachable by proceeding past the goal state.

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== Terminology ==
* FSE - Formulate, Search, Execute.
* Path - A sequence of states, connected by a sequence of actions.
* Initial State - The agent's starting state.
* Successor State - Any state reachable by a single action, from the current state.
* State Space - The set of all states reachable from the initial state, after some set of actions.
** Note: This exclude any states that are only reachable by proceeding past the goal state.

== Formal Approach ==
The following are general steps to formally define a problem:
* Goal Formulation - Determine goal state(s).
* Problem Formulation - Consider the starting values, and valid intermediary states that allow us to meet the goal.
* Implement a search algorithm that returns a solution, as a sequence of actions.

Five components are required for proper problem formulation:
* Initial State
* Actions - The permissible set of actions, for each state.
* Transition Model - Returns the resulting state, using the current state and chosen action.
* Goal Test - A means to check if the agent is in the goal state.
* Action/Path Cost - A numerical value assigned to each state, to determine the optimal action set.

Determining the above might require a state-space graph.<br>
This is a graph that visually depicts all states, linked together by all possible actions at each state.

Note: When defining a problem, it's generally best to define the action set in the simplest possible manner.

== Problem Types ==
Single-State Problem:
* The solution is a sequence of actions, and the agent knows exactly what state it will be in.
* Deterministic, fully observable.

Conformant Problem:
* The solution, if exists, is a sequence of actions, and the agent may have no idea where it is at a given stage.
* Non-observable.

Contingency Problem:
* Percepts provide new information about current state.
* Non-deterministic, partially observable.

Exploration Problem:
* Unknown state space.
* Agent needs to explore to learn about the environment.

Online Problem Solving:
* Solving that uses incomplete environmental knowledge.
* State of environment and agent must be checked at various points (such as after every action) to determine next best action.

Offline Problem Solving:
* Deterministic solving that assumes complete environmental knowledge.
* The solution can be formulated at the start of problem, before taking any actions.

Tree Search Algorithms:
* An "Offline Problem" in which a tree is used to determine the problem solution.
* The tree nodes represent states, the root of the tree is the initial state, and the branches are possible actions from each node (state).
* For more on tree search algorithms, see [[Algorithms/Search Types | Search Algorithms for AI]].

Artificial Intelligence/Basic Problem Solving - Revision history

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