A Similarity Evaluation Technique for Cooperative Problem Solving with a Group of Agents Seppo Puuronen, Vagan Terziyan Third International Workshop CIA-99 Cooperative Information Agents July 31 - August 2, 1999 Uppsala (Sweden)
Dec 21, 2015
A Similarity Evaluation Technique for Cooperative Problem Solving
with a Group of Agents
Seppo Puuronen, Vagan Terziyan
Third International Workshop CIA-99Cooperative Information Agents
July 31 - August 2, 1999Uppsala (Sweden)
Authors
Department of Computer Science and Information Systems
University of Jyvaskyla FINLAND
Seppo Puuronen
Vagan Terziyan
Department of Artificial Intelligence
Kharkov State Technical University of Radioelectronics,
UKRAINE
Contents
The Research Goal Basic Concepts External Similarity Evaluation An Example Internal Similarity Evaluation Conclusions
Goal
The goal of this research is to develop simple similarity evaluation technique to be used for cooperative problem solving based on opinions of several agents
Problem solving here is finding of an appropriate solution for the problem among available ones based on opinions of several agents
Basic Concepts:Virtual Training Environment (VTE)
VTE of a group of agents is a quadruple:
<D,C,S,P>• D is the set of problems D1, D2,..., Dn in the VTE;
• C is the set of solutions C1, C2,..., Cm , that are used to solve the problems;
• S is the set of agents S1, S2,..., Sr , who selects solutions to solve the problems;
• P is the set of semantic predicates that define relationships between D, C, S
Basic Concepts:Semantic Predicate P
P(D ,C ,S )
,if the agent S selects solution C
to solve the problem D ;
,if S refuses to select C
to solve D ;
,if S does not select or refuse
to select C to solve D .
i j k
k j
i
k j
i
k
j i
1
1
0
Problem 1:Deriving External Similarity Values
DC
S
DiCj
Sk
SDk,i
DCi,j
SCk,j
External Similarity Values
DC
S
DiCj
Sk
SDk,i
DCi,j
SCk,j
External Similarity Values (ESV): binary relations DC, SC, and SD between the elements of (sub)sets of D and C; S and C; and S and D.
ESV are based on total support among all the agents for voting for the appropriate connection (or refusal to vote)
Problem 2:Deriving Internal Similarity Values
D C
S
Di’
SSk’,k’’
DDi’,i’’ CCj’,j’’
Di’’
Cj’
Cj’’
Sk’
Sk’’
Internal Similarity Values
D C
S
Di’
SSk’,k’’
DDi’,i’’ CCj’,j’’
Di’’
Cj’
Cj’’
Sk’
Sk’’
Internal Similarity Values (ISV): binary relations between two subsets of D, two subsets of C and two subsets of S.
ISV are based on total support among all the agents for voting for the appropriate connection (or refusal to vote)
Why we Need Similarity Values (or Distance Measure) ? Distance between problems is used by agents to
recognize nearest solved problems for any new problem
distance between solutions is necessary to compare and evaluate solutions made by different agents
distance between agents is useful to evaluate weights of all agents to be able to integrate them by weighted voting.
Deriving External Relation DC:How well solution fits the problem
DC CD P D C S D D C Ci j j i i j k i jk
r
, , ( , , ), ,
DC
S
DiCj
Sk2
DCi,j=3
Sk1
Sk3
Agents
Problems Solutions
Deriving External Relation SC: Measures Agents Competence in the Area of Solutions
The value of the relation (Sk,Cj) in a way represents the total support that the agent Sk obtains selecting (refusing to select) the solution Cj to solve all the problems.
SC CS DC P D C S S S C Ck j j k i j i j ki
n
k j, , , ( , , ), ,
Example of SC Relation
DC
SSk
Cj
D2
SCk,j=4
D1
D4
D3
CDj1 = -3
CDj2 = 6
CDj3 = 0
CDj4 = 1
Agents
Problems Solutions
Deriving External Relation SD: Measures Agents Competence in the Problem’s Area
The value of the relation (Sk,Di) represents the total support that the agent Sk receives selecting (or refusing to select) all the solutions to solve the problem Di.
SD DS DC P D C S S S D Dk i i k i j i j kj
m
k i, , , ( , , ), ,
Example of SD Relation
DC
SSk
Di
C1
SDk,i=2
C2
CD1i = -3
CD2i = 5
ProblemsSolutions
Agents
Standardizing External Relations to the Interval [0,1]
standardizing value value =value value
max(value) - min(value)
-min( )
DC CDDC r
ri j j ii j
, ,,
2
SC CSSC n r
n rk j j kk j
, ,, ( )
( )
2
2 1
SD DSSD m r
m rk i i kk i
, ,, ( )
( )
2
2 1
n is the number of problems
m is the number of solutions
r is the number of agents
Agent’s Evaluation:Competence Quality in Problem Area
Q Sn
SDDk k i
i
n( ) , 1
- measure of the abilities of an agent in the area of problems from the support point of view
Agent’s Evaluation:Competence Quality in Solutions’ Area
- measure of the abilities of an agent in the area of solutions from the support point of view
Q Sm
SCCk k j
j
m( ) , 1
Quality Balance Theorem
Q S Q SDk
Ck( ) ( )
The evaluation of an agent competence (ranking, weighting, quality evaluation) does not depend on the competence area “virtual world of problems” or “conceptual world of solutions” because both competence values are always equal.
Proof
Q Sn
SDn
SD m r
m rD
k k ii
nk i
i
n( )
( )
( ),,
1 1 2
2 1
1
2
2 1n
DC P D C S m r
m r
i j i j kj
m
i
n( ( , , )) ( )
( )
,
1
2
2 1m
DC P D C S n r
n r
i j i j ki
n
j
m( ( , , )) ( )
( )
,
...
...
1 2
2 1
1
m
SC n r
n r mSC Q S
k j
j
m
k jj
mC
k,
,
( )
( )( )
An Example
Let us suppose that four agents have to solve three problems related to the search of information in WWW using keywords and search machines available.
The agents should define their selection of appropriate search machine for every search problem.
The final goal is to obtain a cooperative result of all the agents concerning the “search problem - search machine” relation.
C (solutions) Set in the ExampleSolutions - search machines Notation
AltaVista C1
Excite C2
Infoseek C3
Lycos C4
Yahoo C5
S (agents) Set in the Example
Agents Notation
Fox S1
Wolf S2
Cat S3
Hare S4
D (problems) Set in the Example
S e a r c h p r o b l e m s w i t h k e y w o r d s
D 1F i s h i n g i n F i n l a n d
D 2N O K I A p r i c e s
D 3A r t i f i c i a l i n t e l l i g e n c e
Selections Made for the Problem “Fishing in Finland”
D1
P(D,C,S) C1 C2 C3 C4 C5
S1 1 -1 -1 0 -1
S2 0+ -1** 0 ++ 1* -1***
S3 0 0 -1 1 0
S4 1 -1 0 0 1Agent Wolf prefers to select Lycos* to find information about “Fishing in Finland” and it refuses to select Excite** or Yahoo***. Wolf does not use or refuse to use the AltaVista+ or Infoseek++.
Selections Made for the Problem “NOKIA Prices”
D2
P C1 C2 C3 C4 C5
S1 -1 0 -1 0 1
S2 1 -1 -1 0 0
S3 1 -1 0 1 1
S4 -1 0 0 1 0
Selections Made for the Problem “Artificial Intelligence”
D3
P C1 C2 C3 C4 C5
S1 1 0 1 -1 0
S2 0 1 0 -1 1
S3 -1 -1 1 -1 1
S4 -1 -1 1 -1 1
Result of Cooperative Problem Solution Based on DC Relation
AltaVista, Lycos,NOT Excite, NOT Infoseekfishing in Finland
Lycos, Yahoo,NOT Excite, NOT Infoseek
NOKIA prices
Infoseek, Yahoo,NOT LycosArtificial Intelligence
Results of Agents’ Competence Evaluation (based on SC and SD sets)
… Selection proposals obtained from the agent Fox should be accepted if they concern search machines Infoseek and Lycos or search problems related to “Fishing in Finland” and “Artificial Intelligence”, and these proposals should be rejected if they concern AltaVista or “NOKIA Prices”. In some cases it seems to be possible to accept selection proposals from the agent Fox if they concern Excite and Yahoo. All four agents are expected to give an acceptable selection concerning “Artificial Intelligence” related search and only suggestion of the agent Cat can be accepted if it concerns “NOKIA Prices” search ...
Deriving Internal Similarity Values
Set A Set I
A’
A”
A’I
IA”
A’A”I
A’
A”
a)
Set A
Set I
A’
A”
A’I
JA”
A’A”IJ
A’
A”
b)
Set J
IJ
Via one intermediate set Via two intermediate sets
Internal Similarity for Agents:Problems-based Similarity
D C
SS’S’’D
S’’
S’DS’’
S’D
S S S S S S S D DSD' '' ' '' ' '',
Problems
Agents
Internal Similarity for Agents:Solutions-Based Similarity
D C
SS’S’’C
S’’
S’
CS’’
S’C
S S S S S S S C CSC' '' ' '' ' '',
Solutions
Agents
Internal Similarity for Agents:Solutions-Problems-Based Similarity
D C
SS’S’’CD
S’’
S’DS’’S’C
CD
S S S S S S S C CD DSCD' '' ' '' ' '',
Agents
SolutionsProblems
Conclusion
Discussion was given to methods of deriving the total support of each binary similarity relation. This can be used, for example, to derive the most supported solution and to evaluate the agents according to their competence
We also discussed relations between elements taken from the same set: problems, solutions, or agents. This can be used, for example, to divide agents into groups of similar competence relatively to the problems-solutions environment