Semantic networks of frames are commonly used as a method of reasoning in many problems. In most of these applications the semantic network exists as a single entity in a single process environment. Advances in workstation hardware provide support for more sophisticated applications involving multiple processes, interacting in a distributed environment. In these applications the semantic network may well be distributed over several concurrently executing tasks. This paper describes the design and implementation of a frame based, distributed semantic network in which frames are accessed both through C Language Integrated Production System (CLIPS) expert systems and procedural C++ language programs. The application area is a knowledge based, cooperative decision making model utilizing both rule based and procedural experts

Rodriguez, Tony

Snyder, James

English

NASA Technical Reports Server

DISTRIBUTED SEMANTIC NETWORKS AND CLIPS 
James Snyder and Tony Rodriguez 
CAD Research Unit, Design Institute 
California Polytechnic State University, San Luis Obispo 
Abstract. Semantic networks of frames are commonly used as a method of organizing and 
reasoning in many types of problems. In most of these applications the semantic network exists 
as a single entity in a single process environment. Advances in workstation hardware provide 
support for more sophisticated applications involving multiple processes, interacting in a 
distributed environment. In these applications the semantic network may well be distributed over 
several concurrently executing tasks. 
This paper describes the design and implementation of a frame-based, distributed semantic network 
in which frames are accessed both through CLIPS expert systems and procedural C + + language 
programs. The application area is a knowledge-based, cooperative decision making model 
utilizing both rule-based and procedural experts. 
INTRODUCTION 
Currently, the CAD Research Unit is developing an Intelligent Computer Aided Design System 
(ICADS). The purpose of ICADS is to provide an intelligent environment for cooperative 
computer-based problem solving under the explicit control of the user using architectural design 
as a test environment. ICADS allows a group of distributed, intelligent agents to converse about 
a problem larger than any single agent's domain of expertise or knowledge, and provides advice 
and suggestions to the user as to the state and compliance of the current problem solution. 
From a high-level point of view, ICADS is composed of three major pieces: a 
blackboard, a group of procedural and CLIPS-based expert systems referred to as Intelligent 
Design Tools (IDTs), and a semantic network of frames. The ICADS components run as 
distributed processes in a computer network. 
To facilitate distributed expert system execution, a communication framework was 
developed which allows CLIPS expert systems to assert facts to another expert system under the 
control of a blackboard (Taylor 1990, Taylor and Myers 1990). In addition to controlling the 
IDT communication, the blackboard has a conflict resolver which arbitrates between IDTs. The 
conflict resolver establishes the system accepted values by evaluating suggestions made by IDTs 
and is written in CLIPS. The current working model of ICADS has six CLIPS based expert- 
systems. Each expert is controlled by the blackboard using the communication framework 
described above and are in the following architectural domains: cost, access, lighting, thermal, 
acoustics, and structure (Pohl 1989). 
To allow C+ + programs to participate in the current problem solution, they have to 
communicate with the blackboard and have access to the semantic network of frames at the same 
https://ntrs.nasa.gov/search.jsp?R=19920007367 2020-03-17T12:54:41+00:00Z
level of representation as the CLIPS experts. The remainder of this paper will describe the 
implementation of the distributed semantic network in the CLIPS and C+ + environments and 
illustrate sample uses of the C+ + implementation used in conjunction with CLIPS-based 
experts. 
THE CLIPS FRAME REPRESENTATION 
Within the CLIPS environment, the frame-based representation used in ICADS is implemented 
as a set of CLIPS facts. A frame is a collection of information about a class or object. The 
information is represented in CLIPS with a frame header fact and any number of slot facts. 
Slots can define a particular value of the class or identify a relation to another class. In terms 
of a node-link data structure, the frame is a node and a relation is a link (Barr and Feigenbaum 
1981). It is important to note that this representation does not require any modifications to 
CLIPS--it uses only CLIPS facts (Assal and Myers 1990). Unlike procedural paradigms, it is 
not necessary to locate an entire frame when a piece of information is needed. Only the 
pertinent slots are necessary and are accessed directly through CLIPS pattern matching. A frame 
is represented by a set of facts that have one or more common fields to connect them together. 
Each fact has a keyword in the first field to indicate the type of information it represents. The 
keywords are: FRAME, RELATION, VALUE. The second field has the class name which is 
used to connect all the instances of this class or establish a relation with another class. 
CLIPS Frame Definition 
CLIPS Value Slot Definition 
Figure 1 illustrates the CLIPS fact format of 
a frame header. The class field defines the 
class to which the frame belongs. The in- 
(FRAME < class > < instance > ) 
stance field uniquely identifies the frame. Figure 1 - Frame Fact Format 
Having the frame header in a CLIPS left- 
hand-side pattern is not always necessary, but it is useful in performing operations on the whole 
frame; displaying and deleting are example operations. 
Figure 2 shows the CLIPS fact 
format of a value slot. This slot (VALUE <class > <attribute > <instance > < values > 1 
provides the values for particular 
attributes of a frame. It is impor- Figure 2 - Value Slot Fact Format 
tant to note that these values are 
multifield values and can contain mixed types of data. Note also that the instance of the frame 
in contained within the value slot fact. This allows for direct pattern matching of the frame 
attribute values. 
CLIPS Relation Slot Definition 
Figure 3 illustrates the CLIPS 
fact format of a relation slot. 
This relation slot represents a I I 
has-a relationship. The class1 Figure 3 - Relation Slot Fact Format 
and instance1 fields indicate 
the owning class. The class2 and instance2 fields represent the frame instance which is pointed 
to. 
An Example CLIPS Frame 
An example architectural object is a room or 
space. Figure 4 contains an example space 
frame with several values and relations. The 
relations in the example indicate that the 
LOBBY space has four walls. If a wall of a 
particular space is to be referenced by a rule, 
a pattern similar to the example would be 
used. 
(FRAME space 15) 
(VALUE space name 15 LOBBY) 
(VALUE space perimeter 1 5 108) 
(RELATION space wall 1 5 1) 
(RELATION space wall 15 2)  
(RELATION space wall 15 3) 
(RELATION space wall 15 4) 
Figure 4 - An Example Architectural ~ r a h e  
An Example Rule Using a CLIPS Frame 
C + + IMPLEMENTATION OF FRAMES 
Suppose the building code states that the area 
of all bathrooms must be greater than or 
equal to twenty square feet. The rule in 
Figure to see if the area of a bath- 
room is less than twenty square feet. If the 
area of the space is too small an error riles- 
sage is printed. This simple example illus- 
trates how values slots can be used. Relation 
The purpose of the frame classes is to provide an object-oriented representation for the frame 
facts that are used by the CLIPS IDTs. In order to allow C+ + programs to work with the 
current semantic network that is represented in CLIPS, a representation paralleling the CLIPS 
frame data structures was implemented using C + + objects. Using the class method interface, 
frames can be built explicitly and then added to the network; the user is responsible for creating 
any necessary objects to insert into the frame--relations and values are examples. After the 
frame has been built correctly it is then entered into the semantic network using net class 
(defn.de building-code-check 
(FRAME space ?id) 
(VALUE space name ?id BATHROOM) 
(VALUE space area )id )x&:(< )x 20)) 
=, 
(printout t "The bathroom is too small" crlf) 
slots are used in a simpler manner; the type Figure 5 - An Example Rule Using a Frame 
of relation is included in the left-hand-side 
pattern and can be used to reason about groups of objects at the same time. For example, a rule 
could specify a pattern which references all the walls which belong to the BATHROOM space. 
methods. The following sections explain the structure of the C+ + classes used to represent a 
semantic network of frames. 
The Net Class 
The net class is basically a container class I I 
that represents the entir; semantic network. 
It is composed of frame objects. The net 
class provides several methods for the addi- 
tion, removal, and modification of frames, as 
well as, query methods that allow questions 
to be asked about the network. Figure 6 
shows the class structure of the net class. 
The frames data member is a dictionary of 
frame objects. A dictionary is an associative 
Net DlGtionaty 
array where a name is to an actual kigure 6 - The Net Class Structure I instance of an object. In this case, the frame 
instance name is7mapped to a frame instance 
C + + object. 
The Frame Class 
The frame class is the central component of I 1 
the semantic network. A frame is uniquely 
identified by its name and instance number 
and contains value and relation slots illustrat- 
ed by Figure 7. The frame class has several 
methods that permit the addition, and deletion 
of value and relation slots, in addition to 
methods that return information about the 
frame itself. 
1 Fnme Ust 
f Name: \ hnnm,-~wn,,  ... J 
Instance: 
Ownen: / Dictionary 
The Slot Class 
I I 
Figure 7 - The Frame Class Structure 
owner-name: 
owner-Instance: 
The slot class is an abstract class and is used 
to derive new classes. This provides a stan- 
dard interface for all the slot classes. With 
this class the network can always make cer- 
tain assumptions about the methods it can call 
in any derived slot class. As Figure 8 shows 
there are no actual data members present in 
this class--this is why it is abstract. 
Figure 8 - The Slot Class Structure 
Slot 
The Value Class 
The value class is the C+ + representation of 
the CLIPS value fact and is derived from the 
slot abstract class. This class holds multifield 
values in the form of strings, integers, and 
floating points and provides methods for the 
manipulation of these multifield items. The 
structure represented in Figure 9 shows the 
slot class abstract members as well as some 
additional members--this is an example of 
inheritance in C + + . 
The Relation Class 
The relation class, like the value class, is also 
a direct representation of a CLIPS fact--the 
relation fact; it is also derived from the slot 
class. This relation represents a "has-a" 
relationship, and when this object is inserted 
as a slot in a frame object, it defines the 
relationship between two frames. In addition, 
when a relation is created, the frame pointed 
to by the relation is notified that a relation- 
ship has been established. This allows a 
frame to know what frames have relations 
pointing to it. Figure 10 shows the structure 
of the relation class. 
The Val Class 
The val class is used to represent all the types 
of data that could be used in a value class 
(i.e. strings, integers, floating points). 
Instances of this class are used by the value 
class to store values for value facts; the value 
class has a list of val objects as one of its 
data items. Figure 11 depicts the structure of 
the val class. 
1 1 Name: 1 I 
1 1 
Figure 9 - The Value Class Structure 
Screen: 
Display: 
value-list. 
Relation 
- 
Ust 
-I vaLval.v al.... 
h a :  
Owner: 
Owner-name: 
Owner-instance: 
\ ,' 
Figure 10 - The Relation Class Structure 
Val Union 
type-tag: 
val: 
Figure 11 - The Val Class Structure 
TRE FRAME PARSER 
An alternative to using class methods for constructing a network is the frame parser. The frame 
parser was designed to accept an input language that specifies CLIPS facts in the same format 
as the CLIPS representation uses and is listed in Appendix A. Using the frame parser relieves 
the user from having to create new frame class instances, reducing the complexity of the coding 
effort. This language does not provide queries about the network structure; the user must rely 
on the class methods. 
In addition to the fact information, an action is prepended to the fact to indicate what 
action is to be taken with the fact. There are three supported actions: ADD, MODIFY, and 
DELETE. For example, the keyword ADD is used to add a new frame to the semantic network. 
EXAMPLE USES OF THE C+ + FRAME CLASSES 
Several applications have been written which use the C+ + frames in a procedural environment. 
The applications are suited to an environment such as C+ + much more than an expert system 
shell like CLIPS; these types of programs are not easily expressed using CLIPS. 
The Design Interface 
One of the primary objectives of the Design Interface was to view changes in the semantic 
network as they occurred; the user should be able to specify the desired slots to view, and the 
Design Interface would automatically update the display as values change. 
As facts come from the blackboard, they are parsed using the frame parser, described 
previously, and are stored in the semantic network of the Design Interface. In addition, a 
module in the Design Interface tries to match the incoming fact with a user-specified set of slots. 
If there is a match, the values are then displayed or updated, which ever case is appropriate. 
The Pre-Design Module 
Within the ICADS model the Pre-Design Module (PDM) is used to construct a building design 
starting with collections of objects that represent the spaces that are to be included in the final 
building structure as denoted by the Project Design Object Frames (PDO). The PDOs are 
loaded into the PDM via the frame parser which was described above. 
Once the PDO frames are loaded into the system the PDM can now make queries about 
these frames. The spaces frames (PDO frames that represent spaces) are displayed as circles 
on the screen that the user can select and position as desired. Criteria are then specified by the 
user to assist the PDM in choosing a central space for the layout of the structure. 
After the layout is agreed upon by user the building layout is sent to a CAD system as 
draw commands for display. A geometry interpreter notices these draw commands and sends 
messages, that are basically equivalent to the grammar specified by the frame parser, to a 
blackboard. Once these statements are received by the blackboard and a representation of the 
semantic network is built in CLIPS, the same messages are sent to any IDTs that require them. 
This technique allows IDTs to be written in either C++ or CLIPS. In the case of 
CLIPS the messages sent from the blackboard are placed in the fact list and the CLIPS rules will 
fire accordingly. If C + + is used the messages are handled by the frame parser which will build 
the same semantic network structure using the frame classes. These frame classes can then be 
used by functions written in C +  +. 
CONCLUSIONS 
Executing expert systems in a distributed environment has allowed for an increased level of 
complexity to be introduced. By having multiple representations, both procedural and expert 
system, available to programmers, the appropriate strengths of each paradigm can be used for 
a particular application. 
The experiences of the ICADS project have proven both methods of representation to be 
useful. The sample applications discussed previously would not be as easily implemented in the 
CLIPS environment. Although, the introduction of the object paradigm to CLIPS 5.0 may 
remove the need to implement the C+ + frames under certain circumstances. However, CLIPS 
5.0 will never completely replace the C + + frame representation; the C + + representation 
allows existing applications to interface into the semantic network in an intuitive and direct 
manner. 
REFERENCES 
Assal, H. and L. Myers (1990). An Implementation of a Framebased Representation in CLIPS. 
First CLIPS Conference Proceedings, Houston, Texas. pp. 570-580. 
Barr, A. and E. Feigenbaum (1981). Frames and Scripts. The Handbook of Artificial 
Intelligence. Vol. I., William Kaufmann, Stanford, CA. 
Coyne, R. C., M. A. Rosenman, A. D. Radford, M. Balachandran and J.S. Gero (1990). 
Knowledge-Based Design Systems. Addison-Wesley, Reading. 
Giarratano J., G. Riley (1989). Expert Systems: Principles and Programming. PWS-Kent, 
Boston. 
Myers, L. and J . Pohl (1 99 1). Computer-Based Intelligent Design Assistance: Concepts and 
Strategies. First International Conference on Artificial Intelligence in Design. 
Edinburgh, Scotland, U.K. 
NASA (1989). CLIPS Reference Manual: Version 4.3 of CLIPS. Artificial Intelligence Section, 
Lyndon B. Johnson Space Center. Houston, TX. 
Pohl, J., L. Myers, A. Chapman, L. Chirica, J. Snyder, H. Assal, J. Taylor, C. Johnson, D. 
Johnson (1990). Knowledge-Based CAAD and the CLIPS &pen System Shell. Technical 
Report, CADRU-04-90, CAD Research Unit, Design Institute, Cal Poly, San Luis 
Obispo, CA. 
Pohl, J., L. Myers, A. Chapman, J. Snyder, H. Chauvet, J. Cotton, C. Johnson, D. Johnson 
(1991). ICADS Working Model Version 2 and Future Directions. Technical Report, 
CADRU-05-91, CAD Research Unit, Design Institute, Cal Poly, San Luis Obispo, CA. 
Pohl, J., L. Myers, A. Chapman, J. Cotton (1989). ICADS: Working Model Version 1. 
Technical Report, CADRU-03-89, CAD Research Unit, Design Institute, Cal Poly, San 
Luis Obispo, CA. 
Taylor, J. (1990). A Framework for Multiple Cooperating Agents in an Intelligent Computer- 
Aided Design Environment. (Masters Thesis). School of Architecture and Environmental 
Design, Cal Poly , San Luis Obispo, CA. 
Taylor, J. and L. Myers (1990). Executing CLIPS Expert Systems in a Distributed Environment. 
First CZIPS Conference Proceedings, Houston, Texas. pp. 686-695. 
APPENDIX A - Frame Parser Grammar 
f a c t  : 
YaDD a d d a c t i o n s  
I YMOD modact ions  
I YDEL d e l a c t i o n s  
i 
a d d a c t i o n s :  
YFRAME f r a m e g a r t  
I YVALUE v a l  u e g a r t  
I 
I YRELATION r e l a t i o n g a r t  
i 
modac t ions :  
YVALUE v a l  u e g a r t  
i 
d e l a c t i o n s :  
YFRAME f r a m e g a r t  
I 
I YVALUE v a l  u e g a r t  
I 
I YRELATION r e l a t i o n g a r t  
i 
f  r a m e g a r t  : 
c l a s s  i n s t a n c e  
i 
v a l  u e g a r t  : 
c l a s s  a t t r i b u t e  i n s t a n c e  v a l u e - l i s t  
i 
r e l a t i o n g a r t  : 
c l a s s  c l a s s  i n s t a n c e  i n s t a n c e  
i 
v a l u e - l i s t  : 
v a l u e  
I v a l  u e - l i s t  v a l u e  
i 
v a l u e  : 
YSTR 
I Y INT 
I YREAL 
; 
c l a s s  : 
YSTR 
i 
i n s t a n c e  : 
YINT 
i 
a t t r i b u t e :  
YSTR 
i 


Distributed semantic networks and CLIPS

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