International Carpathian Control Conference ICCC’ 2002 MALENOVICE, CZECH REPUBLIC May 27-30, 2002
KNOWLEDGE BASED SYSTEM IN DESIGN SYNTHESIS Adriana BARNOSCHI1, Vasile PODARU2 and Adriana Meda TRUŢĂ3 1 “N. Titulescu” University, Bucuresti, Romania, E-mail:
[email protected] 2 Military Technical Academy, Bucuresti, Romania, E-mail:
[email protected] 3 Institutul National de Informatii, Bucuresti, Romania, E-mail:
[email protected] Abstract: The process of engineering design incorporates various stages that could be divided into subtasks. The designer examines a few alternatives and makes basic election. This activity is one area that can be considered from the point of view of automation. In this paper the approach of the design stage is brought out as design synthesis that involves the generations of one ore more feasible solutions consistent with the problem requirements. The synthesis problems are formulated as an objective function that contains the constraints. For obtaining an optimal solution we propose a knowledge based system for design description. The approach to design synthesis that could be used for engineering design is the problem reduction (problem decomposition – solution re-composition). The designed artifact is decomposed into a hierarchical network of systems and attributes. The decomposition tree has an AND/OR graph form. Key words: engineering design synthesis, artifact, knowledge based system (KBS), AND/OR graph.
1 Introduction The opened goal of an expert system is the transformation of the human experience into a computer system. To specify this conversion, we use the techniques of the structured system analysis. The structured analysis outputs of an AI project are the system specifications and a prototype of knowledge base. The structured specification represents the document that describes the final model and disjunction of the complex system into smaller mini-tasks, mini-systems, which are easier to specify and build. Design and implementation take it and make it work in the real world.
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In the conceptual design phase, the designer investigates many potential alternatives and makes fundamental choices that will have major impact on the decisions. Creativity is vital for obtaining desirable solutions. Each design parameter may correspond to many options. The important characteristic feature of conceptual design is that several feasible design solutions are developed and evaluated. This area is considered to be an important goal from the standpoint of automation. We know that frequently used representation schemes in KBS (rules, frames etc.) and control mechanisms (forward chaining, backward chaining or mix reasoning) do not provide competent strategies for construction of design models in real-life applications.
2. Design Synthesis for Engineering Design Design synthesis implicates the formation of one or more design solutions that are consistent with the defined requirements and any additional one, identified during this process. At the time of design process the form of the solution is recognized. This phase of the design process is not well supported by computer-based tools, unless the problem can be formulated in mathematical terms. In this case, certain classes of synthesis problems can be formulated as an objective function. We use the constraints and optimization techniques to solve them. These formulations are always nonlinear. Mathematical programming techniques are used for obtaining an optimal solution. It is regrettably also a time when the optimization techniques are not even off the ground of practical problems. The reasons are: a) Programming techniques require a complete definition of the problem; b) Practical considerations require a set of discrete dimensions, etc. Several process for synthesis have been proposed and reported in the literature [1]. For engineering design there are the following three approaches to design synthesis: problem reduction, case based reasoning and transformation. 2.1 Synthesis by problem reduction (problem decomposition - solution re-composition) Also in engineering, design problem can be divided into a set of smaller subproblems. Each sub-problem, in turn, may contain a further set of sub-problems. This representation is called decomposition. Decomposition continues until a sub-problem can be managed without farther decomposition. The sub-problem can be solved by: a selection among a set of possible values or a method or procedure that gives the problem resolution. In this approach, it is necessary to display the problem knowledge in a hierarchical form. In general, this propose implies the identification of physical objects, far-reaching the solution and defining their hierarchy. Then, solutions will be recomposed into coherent whole solution by combining the interaction between subsystems with constraints. 2.2 Synthesis by case-based reasoning In this case, we need anterior cases or examples of designs (solutions) and knowledge about how to transform a previous design (solution), for satisfying the current requirements. A case represents an instance of past design solutions. Cases can be episodic or can represent the result of abstraction and generalization over several episodes. Successful use of the case-based approach needs efficient algorithms and knowledge representation 478
schemes to retrieve relevant cases and transform a past case to suit the requirements of the present situation. 2.3 Synthesis by transformation approach Different the case-based reasoning, which uses specific episodes, transformation uses generalizations. In the transformation model, design knowledge is expressed as a set of reformation rules in which the left-hand side (LHS) of the rule is replaced by the right-hand side (RHS) of the rule. The most usual application of the transformation model is grammar type. The issues associated with using this model are representation of the design description, control in selection of an eligible transformational rule and termination of the application of rules.
3. Decomposition model for synthesis It comes from the idea of dividing large and complex problems into the others, smaller and easier to solve. So, the designed artifact is decomposed into a hierarchical network (like a tree) of systems and attributes. Each system is decomposed into its subsystems and attributes. In the tree, the leaf-level entity is called an attribute. All the other nodes, including the root node are referred to as systems. If we are referring to the systems at the two intermediate levels, then the system from the lower level is referred to as a subsystem of its parent system. The decomposition process goes on until every leaf node is an attribute and its value is assigned or computed. The process of solution generating consists of progressively building up the smaller components from the attribute level to the root. This process of starting at the lowest level and proceeding up the tree assembling systems is called re-composition. The query that arises about knowledge is “Do we decompose what?” Decomposing a problem into sub-problems can be described in two ways: 1. Decomposing into various physical components that are used to built the solution; 2. Decomposing into various functions that must be provided for, by the design solution. The first decomposition can be referred in terms of form, while the second is a function focus approach.
Figure 1: Decomposition tree of system X The knowledge of design contains the knowledge of both types: function and form. The way for eluding the dilemma of decomposition based on function or form, is to ignore the separation and develop a uniform depiction for both function and form. Every node in the decomposition hierarchy is termed as a goal and may represent a function or a form.
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We consider an example of a system X (artifact), as shown in Figure 1, to illustrate the decomposition and the alternate solutions. Every node in this decomposition (denoted in uppercase letters) is a goal. The decomposition tree has the form of an AND/OR graph. The system X is decomposed into attribute Y and system Z, which thus becomes a subsystem of X. The system Z is decomposed into its attributes Z1 and Z2. In synthesis terminology, Y, Z1 and Z2 are the leaf nodes of decomposition tree, so they are the attributes. They can take any one of the values: y1, y2: z1, z2, z3, z4, respectively. There are eight possible solutions to the synthesis problem represented by the tree shown in figure 1, i.e. eight different ways in which system X can be recomposed. These possible solutions are given in Table 1 and the sequence in which the solutions are generated follows the depth-first search. Table 1: Possible solution of system X Solution 1 X.Y=y1 Z.Z1=z1 Z.Z2=z3
Solution 2 X.Y=y1 Z.Z1=z1 Z.Z2=z4
Solution 3 X.Y=y1 Z.Z1=z2 Z.Z2=z3
Solution 4 X.Y=y1 Z.Z1=z2 Z.Z2=z4
Solution 5 X.Y=y2 Z.Z1=z1 Z.Z2=z3
Solution 6 X.Y=y2 Z.Z1=z1 Z.Z2=z4
Solution 7 X.Y=y2 Z.Z1=z2 Z.Z2=z3
Solution 8 X.Y=y2 Z.Z1=z2 Z.Z2=z4
It is desirable to acquire the knowledge to control an incompatible assemblage of individual components. The KB may also contain the rules that help the modification depending on the context. This type of knowledge is called as planning rules. These rules comprise the situations in which a specific reordering of attributes is necessary, or multiple instances of a special system are generated. These pieces of knowledge are referred to as preconditions. They are supplied by the user or by the expert system if the synthesis process takes place in a KBS environment.
4. Conclusions The recent use of knowledge-based systems for synthesis design descriptions has shown promise and has formed the basis for various models that have been proposed for design synthesis. The most general synthesis model is the decomposition one. The synthesis process constructs the solution by assigning values to the attributes at the lowest level in the hierarchical tree and combining them to form systems leading to the artifact. The strategy is a depth-first search because it moves faster into hierarchy before accomplishing a level. In synthesis, the constraints are the preconditions and they have the power of directives for preventing unrealizable solutions.
References 1. 2. 3. 4.
Maher, M.L., Process models for design synthesis, AI Magazine, Winter, 49, 1990. Podaru, V., Inteligenţa artificială şi sisteme expert, Ed. Academia Tehnică Militară, Bucureşti, 1997. Krishnamoorthy, C.S. and Rajeev, S., Artificial Intelligence and Expert Systems for Engineers, CRC Press, New York, 1996. Barnoschi, A., Sisteme expert, Ed. Academia Tehnică Militară, Bucureşti, 2000.
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