The cognitive architecture SOAR is a well known and intensively documented framework for creating Artificial Intelligence agents which contains lots of interesting features like subgoaling, chunking and graph based working memory. But something is wrong with SOAR, which prevents that the software can be recommended for serious application. It has mainly to do with the documentation which are formulated always in the “you can do something”, and “you have to click this button” and so on. This kind of language works similar to the promotional language used in advertisement campaign, in which the new car is simply great, and and all what the user has to do is to get excited.
From an academic standpoint, this kind of description doesn't fulfill the minimum standard, which means, that the SOAR documentation can't be called scientific, but it's a waste of time reading it. Sure, SOAR itself is great, it combines most of the advanced features which are available for modern agent architectures but it's possible to make the overall system much better.
Which means, there is a need to invent things from scratch and built a new cognitive architecture from scratch which is less powerful than SOAR, but provides a better self-description what the project is about. The first step in doing so is to explain what the idea behind SOAR is. In contrast to normal solver, which is searching in the gametree for a goal node SOAR is working with heuristics. That means, the requirement for CPU ressources is low and no high computational task is needed to solve a problem. Everthing in the SOAR universe works reactive and with human engineered knowledge. The concept is sometimes called symbolic AI because no number crunching is involved in the game.
A first approach to understand SOAR better is to compare it with a STRIPS planner. A strips planner contains of actions which can be executed by the solver, and the precondition/postcondition for each action is given in the strips file. A cognitive architecture is some kind of advanced strips planner which has the ability to access a working memory and to learn new goals on the fly.
The Strips notation is a good starting point for developing a cognitive architecture from scratch, because the concept is described in mainstream Gaming AI and it's not very complicated to grasp the basic idea. The game starts with an init state, the solver is trying to find a sequence of actions, and this will guide the robot to the game.
In a paper [1] a mixture was described between a classical HTN planner and a memory architecture. The resulting architecture isn't so powerful like SOAR, bit explains more easier to understand what the idea is. It starts with a vanilla HTN planner for figuring out the actions for the robot. Before the planning process gets started, some operations on the internal memory have to be done. The paper admits, that it has reverse engineered the SOAR architecture, and the similarity is there. Exactly this approach makes sense, because this kind of inventing something twice is equal to learn something. If somebody isn't able to clone a software, he hasn't understand it.
HTN planning plus short term memory
The idea of combining HTN planning with a memory was also discussed in another paper.[2] It describes a non player character in a game, which doesn't have full information about the game, but sees only a limited amount of information which are stored in the short term memory. Similar to the previous referenced paper, the systems starts with a normal HTN planner for generating actions which is extended by additional components like a subgoal generator and a short term memory.
It's interesting to know, that the concept of a working memory plus subgoaling is described as well in the SOAR context as in Game AI non player characters as well. It seems, that life-like characters in games have a natural demand for a working memory and for hierarchical planning capabilities.
Let us imagine, how a non player character can be created which is more flexible. The first thing to do is to realize the working memory not as a normal datastructure but as a semantic network. All kind of variables can be stored there and nodes inbetween the items are possible, similar to a linked list. The second thing to do is to add a learning functionality. Which is a decision tree ID3 learning algorithm. This allows the non player character to adapt his behavior in realtime.`
[1] Zhang, Jun, et al. "MBHP: A memory-based method on robot planning under uncertainties." 2011 IEEE International Conference on Robotics and Biomimetics. IEEE, 2011.
[2] Mahmoud, Ibrahim M., et al. "Believable NPCs in serious games: HTN planning approach based on visual perception." 2014 IEEE Conference on Computational Intelligence and Games. IEEE, 2014.