In the history of AI the well known General problem solver was the first example for an abstract planning system. Later examples were STRIPS and a modern version is called GOAP (goal oriented action planning). What these systems have in common is that they are using rules to solve a problem. A rule is part of a simulation, very similar to what a player activates in a textadventure to reach a goal.
The difference between rules and normal functions in a c program is, that the order of rules isn't fixed. It's the task of the solver to determine the sequence by it's own. This provides a higher flexibility. The simulation can start at a initstate and can be transfered into a goal state. Very similar to what a classcal pathplanner is doing, except that the world isn't a 100x100 pixel map but the state space contains of abstract actions like “take key”, “walkto location” and “open door”.
To get an idea how to use symbolic AI in the reality, a new environment was published in the year 2018 by Microsoft Research called Textworld. https://www.microsoft.com/en-us/research/project/textworld/ It is a textadventure and a solver in the same program. The textadventure simulates a world in which the player can do tasks which have to be entered on the command line. The solver is able to determine the actions autonomously to reach any state in the simulation.
The reason why robotics can profit from this idea is because the state space of a textadventure is much smaller than the geometrical statespace. The amount of possible goals and actions in a symbolic simulation is not very great. A graph search solver will find the actions very fast. The interesting aspect is, that GOAP like solvers doesn't need a certain framework, nor a dedicated STRIPS like programming language. The more important aspect is, to convert a domain into a textadventure. This is the most harder part.
In the famous monkey banana problem the domain contains of only 3 actions and 2 objects. In a robotics domain, the amount of actions is higher. The AI engine for a computergame will need hundred of rules and a dozens of subgoals. This programming task can be handled with the normal software engineering paradigm. That means with the help of UML diagrams, git version control and bugtesting. Or let me explain it from the other perspective. The question is how does a textadventure look like which is about a self-driving car, a biped robot, a Lemmings playing AI or a Mario AI bot? If somebody has answered these question he gets a fully working AI which is planning very fast. Sometimes the concept is called “Task and motion planning”. Task planning is referencing to symbolic AI planning which is equal to GOAP, STRIPS and GPS. Motion planning is the lowlevel side which includes inverse kinematics and pathplanning in a map. The more important part is the task planner, or to be more specific, the textadventure in which possible tasks are formalized.
Text adventure solver
The fascinating information about text adventures is, that they can be solved relative easily automatically. In contrast to a normal computer the amount of possible actions is not billions but only a few thousands. The famous blocksworld example in which a robot arm has to pick and place boxes can be interpreted as a textadventure as well. The robot has certain commands like grasp, ungrasp, moveto and the goal is to find the correct action sequence. It's some kind of puzzle game which can be solved with a graph search algorithm in under a minute.
Unfortunately, most games are not available as a textadventure. For example, Mario AI, Lemmings, or Starcraft are working with graphics but not with text and the available actions are unknown. To overcome the bottleneck one idea is to monitor an existing textadventure if it's fit to a game. That means, the Mario AI original game plus the Mario AI textadventure are started at the same time and the question is, if both instances are in the same state. If not, the textadventure version has to be modified.
Simulation
The Situation calculus is the theoretical background behind the STRIPS planning language. It describes a world which contains of a world state which has actions to change the state. Running such simulation is called a qualitative simulation because it's not based on numerical values but on natural language. For example, if the player types in “open door”, the string of the door variable gets changed into “door is open”. It's important to understand, that a qualitative model isn't an algorithm but it's a text adventure. It forms an environment in which a human player can execute actions and observe the resulting game state. An algorithm is needed only on top of the simulation to bring the system into a goal state.
In the history of AI many techniques were developed to realize a qualitive simulation. One example are the planning languages which are ADL, STRIPS, Golog or PDDL. Another attempt are XML based models and the latest innovation are RDF-triple storage and General description languages. All of these techniques are trying to build a textadventure as easy as possible.