Problem Solving as Modeling.
Problems vary widely, and so do their solutions. Sometimes a problem and its solution are clear, but you don’t know how to get from point A to point B. At other times, you may find it hard to define what’s wrong or how to fix it. Regardless of what a problem is, you can use a six-step problem-solving model to, address it. This model is highly flexible and can be adapted to suit various types of problems. It also comes with a. flexible set of tools to use at each step. The model is designed to be followed one step at a time, but you may find that some stages don’t require as much attention as others. This will depend on your unique situation.
The steps in the problem-solving model are as follows:
Define the problem: Defining the problem is a crucial step that involves digging deeper to identify what it is that needs to be solved.
Analyze the problem: You decide what type of problem it is whether there’s a clear barrier or circumstance you need to overcome, or whether you need to determine how to reach a goal. Identify as many potential solutions as you can Brainstorm creatively ask lots of questions about the who, what, where, when, and how of the causes to point to various possibilities. Choose the best solution, In evaluating your ideas, more options could present themselves.
Plan of action: During this step, you determine what steps must be taken, designating tasks where necessary and you decide on deadlines for completing the actions and estimate the costs of implementing them.
Implement the solution: This is an ongoing process. You need to ensure the required resources remain available and monitor progress in solving the problem.
The Problem Space Hypothesis.
Performance in Soar is based on the problem-space hypothesis which states that: all goal-oriented behavior occurs as search in problem spaces. The context of a goal is specified by three things: a problem space, a current state, and a current operation The problem space for a task domain consists of a set of states representing possible situations in the task domain, and a set of operators that transform one state to another. Problem solving in a problem space consists of starting at some given initial state and applying operators (yielding intermediate states) until a desired state is reached that is recognized as achieving the goal.
Whenever a goal (or sub-goal) is presented to Soar, a problem space is selected in which goal achievement can be pursued. Within a problem space, there is two kinds of search which takes place. One is problem search, which is the search in the state space of the problem. The other kind of search is knowledge search whose task is to find control knowledge to constrain the problem search.