Problem solving using expert system techniques

There are many alternative problem solving strategies that can be implemented using expert system tools and techniques. However, there are basically two approaches to problem solving currently used in expert systems: the derivation approach and the formation approach. This paper begins with a description of a subset of the problem solving strategies used in expert systems and how these strategies support the derivation or formation approach. This is followed by the description of a structural design problem and its implementation using first a formation approach and then a derivation approach. the preconditions that are needed for the hypothesis are set up as subgoals. This process continues until the original hypothesis is either supported or not supported by known facts. The system may then pursue the validity of another hypothesis in the knowledge base. The order in which the hypotheses are pursued is predefined. Mixed Initiative. A system uses a mixed initiative strategy when it combines the forward chaining and backward chaining strategies. The system starts with the initial state of known facts to assign a probability to each of the potential goal states. The system then tries to support the goal state with the highest probability by setting up subgoals and requesting additional information from the user if necessary. In the mixed initiative strategy the order in which the hypotheses are checked depends on the problem at hand. The advantage to this strategy is that the user only supplies the data relevant to the problem at hand and not all possible data values. The problem solving strategies appropriate for implementing a formation approach are: problem reduction, plan-generate-test, and agenda control. These strategies may be supplemented with the concepts of hierarchical planning and least commitment, backtracking, and constraint handling techniques. The development of an expert system using one of these strategies requires the definition of the components of the solution and a description of how the components can be combined. An illustration of the unconnected graph of components is shown in Figure 2. The solution is not completely defined by a goal state, but requires that the solution path be known also. The disadvantage to using one of these strategies is the lack of a standard implementation or expert system tool that employs a strategy appropriate for the formation approach. These strategies are typically implemented using a lower level language such as Lisp. Problem Reduction. Problem reduction involves factoring problems into smaller subproblems. The problem is represented as an ANDOR graph. An AND node consists of arcs pointing to a number of successor nodes, all of which must be solved for the AND node to be true (or solved). For an OR node, it is sufficient for one of the successor nodes to be solved; an OR node indicates that a number of alternate solutions exist for the problem. Plan-Generate-Test. The generate-and-test strategy in its pure form generates all possible solutions from components in the knowledge base and tests each solution until it finds a solution that satisfies the goal condition. The plan-generate-test sequence restricts the number of possible solutions generated by an early pruning of inconsistent solutions. The pruning is achieved by the planning stage, where the data is interpreted and constraints are evaluated; these constraints eliminate solutions that are inconsistent. Agenda Control. The agenda control strategy involves assigning a priority rating to each task in the agenda. The task with the highest priority is performed first. A list of justifications and a method of determining the priority rating is associated with each task. This type of control can be used for complex tasks that require focusing attention on certain parts of the problem Agendas can also be used in systems that require several independent sources of expertise to communicate with each other. Hierarchical Planning & Least Commitment. The concept of hierarchical planning involves the development of a plan at successive levels of abstraction. For example, in the design of a complex system the design space is divided into a set of levels, where the higher levels are abstractions of the details at lower levels; the problem is hierarchically decomposed into