AAMAS 2009 • 8
th
International Conference on Autonomous Agents and Multiagent Systems • 10–15 May, 2009 • Budapest, Hungary
1330
Section 3, we illustrate its adaptability with some experiments.
Section 4 draws our conclusion.
2. EVOLUTIONARY ORGANIZATIONAL
SEARCH
Employing parse trees as genotypes, genetic programming is a
metaheuristic method for evolutionary optimization of programs.
STGP adds type consideration to conventional GP. Evolutionary
Organizational Search extends STGP to suit the needs of
organizational optimization by introducing a modeling method
and novel genetic operators.
In genetic programming, each symbol or node is either a terminal
or a non-terminal. EOS extends the above two categories to five
categories, namely GroupNode, AgentNode, ResourceNode,
RoleNode and NullNode. GroupNode and AgentNode are
nonterminals. ResourceNode, RoleNode and NullNode are
terminals. These categories of nodes provide modeling guidelines,
simplify manipulation of the tree structure and ensure its integrity.
A group of agent is represented as a tree/subtree, of which a
GroupNode serves as the root. An AgentNode represents an agent
which may access or control resources. Thus, a ResourceNode,
representing a resource, may be a child of either a GroupNode or
an AgentNode. An AgentNode that does not have any
ResourceNode children should have a single NullNode child. The
RoleNode is used when multiple roles is taken by one agent. It
represents a role and points to an AgentNode taking up that role.
In the strongly typed approach, each node is assigned a returned
type. Non-terminals can specify types of arguments as a set of
allowed returned types for their children. A parent-child
relationship can be established only when a matching is found.
EOS supports hard constraints on the number of specific types of
nodes. In most applications, computational and other resources are
bounded in an MAS. Additionally, we may want to utilize at least
a certain number of resources, such as databases in an information
retrieval system. In EOS, traditional tree growth, mutation, and
crossover operators are modified to account for lower and upper
limits on the number of ResourceNode and AgentNode.
A new mutation operator, the redistribution operator, is also
proposed to complement the traditional point mutation. In the
optimization process, it is often desired to fine-tune the allocation
of resources between agents and groups of agents. This operator
randomly mutates the combination and groupings of
ResourceNode and AgentNode in the tree to accelerate such
optimization. Due to space constraints, however, we cannot
present detailed algorithms for the genetic operators in this paper.
3. EMPIRICAL STUDIES
In the experiments we optimize the organization of an information
retrieval (IR) system. The IR system is hierarchical, consisting of
three agent roles, including agents which control databases,
aggregators which collect data from lower agents and route
messages, and mediators which accept user queries and decide
which portion of the system to search. A group of mediators, in
charge of aggregators and databases below it, sit at the topmost
layer of the system and cooperate to answer user queries. Readers
are recommended to read [2] for detailed description of the system
and environmental variables.
The system performance is affected by two factors: recall rate and
response time. The recall rate is determined by the relevant data
being searched compared to total relevant data in the system. The
utility value to be optimized is computed as follows.
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Three sets of experiments with different environmental variables
were repeated for 50 times. Fifty individuals were evolved for 20
generations. Table 1 shows how the organizational structure of a
system managing 15 databases adapted under different
environment. Among various organizational features, we are
mainly interested in the number of mediators. The results suggest
a trade-off between recall rate and response time. When the
system was not queried very often (0.05), a single mediator that
administered all databases could cope well, but when the query
rate was increased to 0.10, multiple mediators that share the
workload became necessary. In the third setting, when the system
searched only databases under a single mediator, the number of
mediators was decreased so as to maintain a good recall rate.
Table 1 Empirical Results
Query
Rate
Max
Search
Set
Size
Number of
Mediators
Fitness
Average Std Dev Average Std Dev
0.05 4 1.00 0.00 851.22 1.47
0.10 4 4.74 0.44 468.17 13.84
0.10 1 2.00 0.00 346.52 4.91
4. CONCLUSIONS
In this paper we proposed a novel framework of evolutionary
optimization for agent organizations based on strongly typed GP,
which we name EOS. It fills the gap between existing quantitative
optimization frameworks by providing a metaheuristic
optimization technique. EOS also considers special constraints
and requirements of MASs and can be applied to a wide range of
tree-shaped organizational forms. Our experiments generated
differently structured organizations which adapted to different
environments for an information retrieval system.
5. REFERENCES
[1] Dignum, V., Vazquez-Salceda, J. and Dignum, F. Omni:
Introducing social structure, norms and ontologies into agent
organizations. Proceedings of the Third International Joint
Conference on Autonomous Agents and Multi-Agent Systems
(New York, NY, 2004), 91-102.
[2] Horling, B. Quantitative Organizational Modeling and
Design for Multi-Agent Systems. PhD Thesis, University of
Massachusetts at Amherst, 2006.
[3] Horling, B. and Lesser, V. A survey of multi-agent
organizational paradigms. Knowl. Eng. Rev., 19, 4 (Dec.
2004), 281-316.
[4] Montana, D. J. Strongly typed genetic programming. Evol.
Comput., 3, 2 (Summer 1995), 199-230.
[5] Sims, M., Corkill, D. and Lesser, V. Knowledgeable
Automated Organization Design for Multi-Agent Systems.
Technical Report 07-43, University of Massachusetts,
Amherst, MA, 2007.
[6] So, Y.-p. and Durfee, E. H. Designing organizations for
computational agents. In Simulating Organizations. AAAI
Press/ MIT Press, 47-64, 1998.