Evolutionary Organizational Search
(Extended Abstract)
Boyang Li Han Yu Zhiqi Shen Chunyan Miao
Nanyang Technological University
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In this paper, we proposed Evolutionary Organizational Search
(EOS), an optimization method for the organizational control of
multi-agent systems (MASs) based on genetic programming (GP).
EOS adds to the existing armory a metaheuristic extension, which
is capable of efficient search and less vulnerable to stalling at
local optima than greedy methods due to its stochastic nature.
EOS employs a flexible genotype which can be applied to a wide
range of tree-shaped organizational forms. EOS also considers
special constraints of MASs. A novel mutation operator, the
redistribution operator, was proposed. Experiments optimizing an
information retrieval system illustrated the adaptation of solutions
generated by EOS to environmental changes.
Categories and Subject Descriptors
I.2.11 [Artificial Intelligence]: Distributed Artificial Intelligence
Multiagent systems. G.2.1 [Discrete Mathematics]:
Combinatorics – Combinatorial algorithms.
General Terms
Algorithms, Performance, Design.
Experimental, Systems, Biologically-Inspired Approaches,
Organizational Planning, Genetic Programming, Multi-Agent
By specifying important aspects of a multi-agent system such as
role assignments, hierarchies of authorities and relationships
between agents such as information flow and collaboration,
organization may significantly influence the efficiency,
adaptability and robustness of MASs [2, 3, 6]. Optimal design of
MAS organizations has been an active field of research. In this
work, we keep our focus in optimizing organizational control
rather than operational control. In other words, we are interested
in long-term guidelines such as authorities, role assignments and
responsibilities rather than exact algorithmic instructions for
specific tasks like communication protocols or social norm
A number of methodologies, such as Omni [1], etc., attempt to aid
the human designer in organizational optimization. They walk
designers through the entire design process and cover both
organizational control and operational control, but perform only
qualitative analysis and ultimately require human judgments. In
contrast, with a focus on organizational control, EOS performs
automated quantitative analysis, which may produce convincingly
justified results and ease the manual burden.
Existing methods for quantitative optimization of MAS
organization include [2], [6], and KB-ORG [5]. These methods
differ in their use of domain knowledge and scope of search. In [2,
6], domain knowledge is primarily utilized as explicit or implicit
organizational templates which provide holistic views of the MAS.
Solutions are searched in the generate-and-evaluate approach. In
[2], a general modeling language, the Organizational Design
Modeling Language (ODML), is proposed and the template is
explicitly constructed in it. It performs exact global searches,
employing techniques such as backtracking, and equivalence class.
In comparison, KB-ORG [5] represents domain knowledge as
multiple small fragments to make a series of locally optimal
decisions, and is thus greedy. However, as the optimization
complexity has been shown to be NEXP-complete [2], it is
generally infeasible to apply exact search methods in
optimizations of MAS organizations. On the other hand, we may
be interested in the global optimum, which greedy algorithms
might not provide in nonlinear and non-monotonic search spaces
[2]. As a consequence, it is necessary to develop a heuristic global
search method for organization optimization.
In this paper we propose an optimization method called
Evolutionary Organizational Search (EOS) based on strongly
typed genetic programming (STGP) [4] as an addition to the exact
search methods in [1]. EOS optimizes tree-structured
organizations and is well suited for hierarchical organizations as
well as other tree-like organizational forms such as holarchies and
federations. The interested reader is referred to [3] for a survey of
MAS paradigms. During the optimization, constraints on
resources are taken into account, and special genetic operators are
also designed, so as to cope with needs of real-world MAS.
Furthermore, due to the stochastic nature of genetic programming,
it has the potential to find the global optimum and outperform
greedy methods. EOS can be easily implemented as a distributed
algorithm, and its execution may be stopped anytime. Hence it is
suitable for self-adaptation of multi-agent organizations.
KB-ORG emphasizes a knowledge-based, formal and complete
design process and the existing methods in [2] focuses on
capturing system performance and building models, EOS fills in
the gap in between by proposing a method for efficient search.
In the next section, we describe the representation and genetic
operators of EOS. Nevertheless, the space constraint forbids the
inclusion of formal definitions and complete pseudo-codes. In
Cite as: Evolutionary Organizational Search (Extended Abstract), Boyang
Li, Han Yu, Zhiqi Shen, Chunyan Miao, Proc. of 8th Int. Conf. on
Autonomous Agents and Multiagent Systems (AAMAS 2009), Decker,
Sichman, Sierra, and Castelfranchi (eds.), May, 10–15., 2009, Budapest,
Hungary, pp. XXX-XXX.
Copyright © 2009, International Foundation for Autonomous Agents and
Multiagent Systems (www.ifaamas.org). All rights reserved.
Cite as: Evolutionary Organizational Search, (Extended Abstract), Boy-
ang Li, Han Yu, Zhiqi Shen, Chunyan Miao, Proc. of 8th Int. Conf. on
Autonomous Agents and Multiagent Systems (AAMAS 2009), Decker,
Sichman, Sierra and Castelfranchi (eds.), May, 10–15, 2009, Budapest,
Hungary, pp. 1329–1330
Copyright © 2009, International Foundation for Autonomous Agents
and Multiagent Systems (www.ifaamas.org), All rights reserved.
AAMAS 20098
International Conference on Autonomous Agents and Multiagent Systems 10–15 May, 2009 Budapest, Hungary
Section 3, we illustrate its adaptability with some experiments.
Section 4 draws our conclusion.
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.
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.
ݑݐ݈݅݅ݐݕ ݎ̴݈݈݁ܿܽݎܽݐ݁ ͳͲͲͲ ݎ݁ݏ݌݋݊ݏ̴݁ݐ݅݉݁ȀͳͲ
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
Number of
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
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.
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