A Comparison of Language Evolution and
Communication Protocols in Multi-agent Systems
Dirkjan Bussink
University of Twente, Faculty of Electrical Engineering, Mathematics and Computer Science
[email protected]
ABSTRACT
Communication is a very important concept in multi-agent
systems. There are several approaches to how this
communication can take shape. In this paper the two most
important approaches are reviewed and compared,
communication using communication protocols and
communication using an evolving language. Both techniques
have their advantages and disadvantages. In industrial
applications communication protocols will be the best
practice, but in systems where homogeneous agents can work
together language evolution is a good option.
Keywords
Multi-agent communication, agent communication languages,
language evolution, communication protocols
1. INTRODUCTION
Communication is a key concept in multi-agent systems. A lot
of research is already conducted in this area, but many ideas
and concepts are still open for debate. The main focus of all
this research is on the classical area of communication
protocols, which are the oldest and most used technique.
Many of these researches are about the formal specification of
the design and implementation of communication protocols,
in order to construct formal and error free protocols [MSH02].
Other researchers have chosen for a totally different approach.
They treat communication just like another component that
can be learned by agents. A widely known research about this
subject is the Talking Heads experiment [Ste99]. Agents learn
and become intelligent though the communication with other
agents.
The aim of this paper is to gain more insight into both
different approaches and to compare them on different
criteria. These criteria can be criteria like interoperability and
complexity. First both methods will be described in general,
to gain a good insight into the working of both techniques.
After this, a comparison will be made, based on the individual
aspects of each method. These individual aspects will be used
to make a general comparison and to conclude which
approach works best for which domain or application. Section
2 will contain a general description of communication in
general and of communication protocols and evolving
language is specific. Section 3 will describe the different
advantages and disadvantages of each method, after which
section 4 will provide a comparison based on these
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Copyright 2004, University of Twente, Faculty of Electrical
Engineering, Mathematics and Computer Science
advantages and disadvantages. Section 5 will conclude the
paper and section 6 will presents some suggestions for future
work.
2. COMMUNICATION IN MULTIAGENT SYSTEMS
Communication is a very important concept in multi-agent
systems. Without it, different agents cannot know from each
other who is doing what and how they can cooperate.
Therefore communication is a must if you want to set up a
useful multi-agent system.
2.1 Theory and ideas behind
communication
Most concepts that are used when agents try to communicate
come directly from research about how humans communicate.
This Speech Act Theory [Aus62] is widely used in
communication protocols, but also evolving languages are
based on human communication. A lot of researchers use this
Speech Act Theory as the basis for their protocol design. This
means that communication can be viewed in the same way as
any other act. If the performance of an agent is analyzed,
communication is treated in the same way as any other action,
such as picking up an object. Both actions can have a positive
impact on the agent’s desired result; there is no difference in
how this impact is rated.
Speech Act Theory divides communication up into different
types of communication messages [RN95]. These different
actions are the following:
•
Inform
Making a statement about yourself or the world,
these are treated as facts
•
Query
Ask other agents whether they have some specific
knowledge of the world
•
Answer
In response to a query of another agent, an agent
can answer that question
•
Request or command
Ask or command another agent to perform a certain
action.
•
Promise or offer
State you will do something, or offer to do
something in return of a favour
•
Acknowledge
Respond as a reaction to offers and requests.
•
Share
Used to share feeling and experiences with other
agents, thus the sharing of subjective ideas and not
of facts
the Speech Act Theory and therefore not present in ACL’s
such as the FIPA ACL. This shows that designing a protocol
is often a very application specific task.
Protocols are often based on these primitive acts. Some of
these acts are used to transfer information the hearer, others
are used to make the hearer perform a certain action.
When a communication protocol is used in a multi-agent
system, the design will all take place before the protocol is
put to its definitive use. The implementation and testing will
therefore take place before the system is made operationally.
When it appears that a certain protocol is not sufficient, the
system has to be taken offline, modified and put back to work.
This can be a very time-consuming process. The research of
Mazouzi, El Fallah and Haddad proposes a formal method for
designing protocols based on Agent Unified Modelling
Language (AUML) and Coloured Petri Nets (CPN) [MSH02].
The protocols designed using these methods are verifiable and
consistent.
When different agents want to communicate, there are in
principle two possibilities. The first and most simple solution
is to directly send information from the knowledge base to the
other agent. This knowledge base is an internal representation
of the agent’s knowledge of the world [RN95]. This is socalled telepathic communication. Both agents participating in
the conversation must have a knowledgebase with exactly the
same structure for this to be possible. If the agents have
different knowledge representation languages, the knowledge
bases cannot be interchanged. This way of communication
could be used by a malicious agent to corrupt the knowledge
base of another agent by simply telling it the wrong
information.
The other way is using some kind of formal language. This is
usually done using a formal grammar, such as a recursively
enumerable grammar or a context-free grammar. This way,
the agents do not have to have the same internal structure.
This approach is nowadays used most of the time.
2.2 Communication protocols
For a long time, the only way agents communicated was using
communication protocols. Therefore research often focussed
on this area and a lot of specifications have been written.
Because of the formal nature of protocols, there are a quite a
few widely known and used standards. First I discuss some of
the organizations and their standards.
The best-known organization that promotes a standard is the
Foundation for Intelligent Physical Agents (FIPA) [FIPA].
They aim to produce software standards for heterogeneous
and interacting agent-based systems. Part of these standards is
the specification of communication protocols that can be used
between agents. One of these specifications is the FIPA Agent
Communication Language (ACL) Message Structure
Specification [ACL]. This specification specifies how
messages should be exchanged between agents and how the
content of the messages should represented. There are a lot of
other specifications from the FIPA that all cover a certain area
of multi-agent communication.
The FIPA specifications are of course not the only
specifications. Other communication protocols are the
Knowledge Query and Manipulation Language (KQML)
[KQML] and the Interagent Communication Language (ICL),
part of the Open Agent Architecture (OAA) [OAA], a
framework for creating your own multi-agent systems. These
last two are more focussed on software agents, whereas the
FIPA is oriented towards physical agents.
Although there a several standards for designing an ACL,
much research is done concerning more specific applications.
Many different researches are dedicated to finding the best
solution for a certain specific application. Examples are a
KQML based brokerage protocol [PGS00] and a collaborate
communication to instruct a robot to conduct certain actions
on objects [SP99]. In the latter article new aspects of
communication are also used, such as indirect speech acts and
object references. These are possible because the agent and
the human are both present in the same environment. These
aspects are modelled into their model, but are not described in
Huget and Koning also suggest a more formal approach to
protocol design [HK03]. They propose several protocol
development stages, much like the software engineering
cycle. The design starts with a requirements analysis, after
which a formal description, validation, synthesis, and
conformance will be performed.
To illustrate how exactly a protocol looks like, an example
will be given based on the FIPA specification [FIPAACL]. It
will describe the simple elements and how they can be
combined into a protocol. A basic FIPA message has several
different messages parameters. The first parameter is the so
called performative, this indicated the kind of
communicative act. There is also a FIPA standard in which
these performatives are listed. The performative parameter
is obligatory. Other important parameters are sender,
receiver and content. Also there is a language
parameter; this indicates the formal language of the content.
A basic message could look like this:
( request
:sender a
:receiver b
:language ComLanguage
:content (action buyStock)
)
This message means that sender a request agent b to buy
stock. The content of the message can be understood by b,
because it will be part of the ComLanguage (just an
exemplary name) formal grammar.
2.3 Evolving languages
The basis for language evolution is in human communication.
The agent languages consist of grammars and vocabularies,
just like any human language. Some researchers even do
research in the area of language evolution using agents in
order to get more understanding of how human
communication has evolved. The Talking Heads research
from Luc Steels [Ste99] also has this goal: “The goal of the
talking heads experiment is not to demonstrate an artificial
intelligence with the same capacities as human intelligence,
but to perform scientific experiments so as to examine aspects
of a theory of the origins of language and meaning” ([Ste99]
p. 10). The evolution of the language than can be compared
to self-organization, such as the creation of a path in an ant
society.
When different agents first start communicating with each
other in a certain environment, both their vocabulary and
grammar are empty. This means the agents will start without
any knowledge, and therefore these ‘virgin’ agents will learn
from scratch.
The exact workings of this principle of evolving languages
will be illustrated using an example from Luc Steels [Ste96].
In his experiment he is using a so called language game in
order to evolve a language. He has used this same idea in his
Talking Heads experiment [Ste99]. The language game is
based on a very simple principle. In a certain environment
there are several agents with different properties (properties
such as colours, weight, etc). The goal is to let each agent
know what the properties are of the other agents. In order to
achieve this goal, they use an evolving communication
language. Each agent tries to communicate its features and
tries to understand the features of the other agents. Agents can
also talk to each other about features from a third agent.
•
•
The language games conducted by Luc Steels are not the only
way of creating a language using evolution. A totally different
approach is to use genetic algorithms and let the best
communicating agents reproduce. This is done in the research
from Jim and Giles [JG01]. The grammar used in this article
is very simple and predefined; the language consists of words
from a certain length N made up from binary characters. In
this case, only the vocabulary of the agents is empty. The
agents do not use a new word for a new situation, but instead
they use random words. Other agents will interpret these
random words, before performing an action. The best
performing agents will then be selected to reproduce. Some
mutation will occur because of the genetic algorithm and this
will be repeated several times. The vocabulary is built this
way totally independent from the system’s designer.
•
3.1 Communication protocols
3.1.1 Advantages
•
Simplicity
This is one of the major advantages of communication
protocols. Because of their strict and formal nature, they
are very easy to understand. When a protocol does not
suffice, the problem can be easily identified and the
protocol can be extended. Due to the formal nature,
reasoning about communication protocols is also very
well possible.
Verifiability
Closely related to the simplicity is the verifiability.
Because of the transparency of a communication
protocol, it is possible to formally analyse the protocol.
This means it is guaranteed to work correctly for the use
it was designed for.
3.1.2 Disadvantages
•
Inflexibility
A communication protocol has to be designed by a
human designer before it is ever used by the agents.
After this design the multi-agent systems cannot adapt
the language when new situations arise. Using a
communication protocol can therefore be very inflexible.
Even when agents are used that learn for themselves,
they cannot adapt their language and are much more
limited.
•
Hard to design
A protocol is easy to design, but designing a good
protocol takes a lot of effort. The inflexible nature of
communication protocols has as a result that the design
of the protocol must be very complete. The agents must
have enough possibilities to communicate all the
necessary information. When it becomes clear after a
while that the designed language is not sufficient, it has
to be revised and tested again. This can be a painstaking
process, before the language is finally satisfactory. When
the agents have to communicate with other agents who
use their own protocol, this design becomes even harder.
3. ADVANTAGES AND
DISADVANTAGES OF DIFFERENT
TECHNIQUES
Every technique used to communicate between agents in a
multi-agent system has its own advantages and disadvantages.
These advantages and disadvantages of both communication
protocols and evolving languages will be discussed in this
section.
Widely known
Because communication protocols are a relatively simple
form of communication, it was the first technique that
was really used in communication. The evolving
languages appeared at a later stage. Because of this,
communication protocols are widely spread and known,
therefore they are also used more often. They are more
thoroughly researched and more information is known
about communication protocols.
When a certain feature is not yet known, a new word will be
made up for this certain property. Agents who do not
recognize this new word will try to associate it with certain
properties. This game is repeated several thousand times
before the language will finally stabilize.
While the language game is being played, agents can be
added and removed from the population. When new agents
are introduced, they will pick up the existing language. When
these new agents have new properties (such as a property
specifying their size), these new properties have to be named
too in order to be able to determine these properties. The
evolutionary process will continue until these properties are
also known. This means that these newly introduced agents
will learn the existing language much faster that the agents
who learned with only ‘virgin’ agents around them.
Interoperability
One of the major goals of protocol specification such as
the FIPA specification is the possibility to let multiple
agents created by different people communicate. This
advantage is very important for the practical application
of agents. In a controlled scientific environment they
may only have to communicate with similar agents, but
in the real world it is very well possible they will have to
work together with other agents.
•
Interoperability
It may seem strange, but interoperability is also a big
disadvantage. When a protocol designer want to adhere
to the FIPA standards, it is not very hard. Because of the
loose formulation of the standards, it is possible to create
a protocol that is compliant, but the agents using the
designed protocol cannot interact with other agents that
do not speak the exact same protocol. A simple example
from [PM99] shows this very clear:
( inform
:sender s
:receiver r
:language KQML
:content (:tell : : : )
)
A human designer only designs the very basic systems
needed to be able to learn. Basic systems such as
perception are implemented. Also parts such as the
vocabulary will be implemented, but only to be able to
add new words. They will not be filled with any default
data. After the systems starts to evolve, the human
designer does not do anything anymore. This may result
in highly complex grammars, which no human would
ever design, but who are very efficient. The system is
capable to exceed its basic design. One example of this is
the experiment conducted by Jim and Giles [JG01]. They
used evolutionary algorithms to randomly create a
language and then pick the best performing agents, who
are then used in another evolutionary cycle. The final
language these agents evolved is very efficient and far
more complex that a human would probably design. The
language has a lot of words, where the meaning also
depends on the state of the agent. A human designed
would probably use different words for different
meanings and not the same word with different meanings
depending on the state of the agent.
This is a simple FIPA compliant message from agent s to
agent r. The content contains the message. In this
example this message contains an entire protocol in
itself, the FIPA compliant message is only used as an
envelope. This means that the protocol itself is FIPA
compliant, but it will never be interoperable with other
systems, because of the custom protocol that in used in
the tell structure. On the other hand, you can also over
specify a protocol. When there are a lot of performatives
that are not known to other agents, they different agents
can also not communicate.
•
Centred around sender
Speech Act Theory is mostly criticised for being centred
too much on the sender. The classification that Searle
made [Se69] made for his view on speech only takes into
account the beliefs and intentions of the speaker. The
theory is therefore very unidirectional. The sender does
not take in account how the hearer will understand the
message, the hearer might think the speaker meant
something totally different and respond it an unexpected
way.
•
Choosing a standard and how formal it should be
interpreted
3.2.2 Disadvantages
•
This property has also been seen as an advantage, the
system can grow more complex that is humanly
designable. This also has a great disadvantage. When the
system is working and has learned quite a lot, it is very
likely that it is not possible, or very hard, to understand
the language that has evolved. This disadvantage can
also been seen in [JG01]. The language is very efficient,
but also very hard to understand. The same word may
have different meanings, depending on the state of the
agent. In this experiment is it possible to use a Mealy
machine to represent the entire language, but when a
larger system is built where the language is more
complex that only one word consisting of a fixed number
of binary digits, this approach will probably not work.
The resulting state machine would be far too large to
draw and the language will be almost impossible to
understand.
When multi-agent systems where first devised, everyone
designed and used their own communication protocols.
Later several standards where developed, KQML is one
of the older ones, FIPA came later. These multiple
standards result in more different protocols and therefore
less compatibility. To make it even more complex, each
standard has some sub standards, such as the FIPA
standard for performatives (which can be used with the
FIPA standard for ACL messages). But you can also
choose to use KQML as a formal language describing
the content of a FIPA compliant ACL message. All these
possibilities make it extremely difficult to design a good
protocol.
3.2 Evolving languages
3.2.1 Advantages
•
Flexibility
One of the main advantages of an evolving language is
its power to adapt. As already illustrated in the example
in section 2.3, agents can devise their own new words
and use them to describe previously unknown concepts.
•
Close to human evolution
The idea that human communication and intelligence
reinforce each other is widely accepted. Whether humans
use language because they are intelligent, or the other
way around is still subject of much debate. Fact remains
that communication is a very important factor when one
tries to create an artificially intelligent agent. The
Talking Heads Experiment has exactly this motivation as
one of the reasons why the research was conducted. This
advantage is of course only relevant if the mimicking of
human intelligence is the goal of the research.
•
Evolution of systems not designable by humans
Table 1: Overview of comparable advantages and disadvantages
Can evolve into a highly complex system
•
Interoperability
Agents, who use their own evolved language, will most
likely not be able to communicate with other agents.
Each evolving language seen so far has a different
structure and works very different. The language games
from [Ste96] are totally different from [JG01]. If the
agents should be able to communicate, they would have
a compatible language, especially a compatible
grammar.
Another problem with heterogeneous agents is
mentioned by [Ste99]. In the Talking Heads Experiment,
all agents have the exact same physical body with the
same perception possibilities. He has thought of using
possibly different physical agents, where some might not
have the capability to see and where other could not
move. This would introduce interesting aspects, but
would make the entire experiment very complex,
Property
Communication protocol
Language evolution
Flexibilty
Flexible in design phase, not when implemented
Very flexible in use, system constantly adapts
Complexity
Fixed complexity
Can grow very complex
Interoperability
With a very good design, the system can work with
other heterogeneous agents
Very hard to make a system able to communicate
with heterogeneous agents
because the agents cannot talk about their perceptual
experiences.
•
Time consuming evolution
In the example in section 2.3 the time necessary for
training the agents was mentioned. The simple language
game already took several thousand iterations before the
language stabilized. When an evolving language is put
into use in a more real-life environment, this could mean
that the evolution could take enormous amounts of time,
because of the complexity of the environment. This is a
serious disadvantage of evolving languages, the system
has to be run for a long time before reliable
communication can be possible.
•
Verifiability
A disadvantage closely related to the possible
complexity of the system, is the verifiability of the
system. When an evolving language is used, there is not
control on how the language evolves. The final language
can be very hard to understand. Because of this,
verification of this language is even harder, if not
impossible. The language is not transparent and its
workings cannot be checked.
4. COMPARISON OF THE DIFFERENT
TECHNIQUES
4.1 Advantages vs. disadvantages
In table 1 there is a short overview of the comparable
advantages and disadvantages mentioned in section 3. The
first major difference between the two different techniques is
the flexibility. Communication protocols are far more rigid
and less flexible than an evolving language. This however,
does not mean that an evolving language is always better. The
flexibility comes at a certain price. The evolved language will
likely be much harder to understand and it will not be easy to
analyse its structure.
This complexity due to the evolving language has a very
important impact. Most of the agents that use an evolving
language are agents that interact among themselves in order to
achieve a certain goal. This is often in a setting that permits
some sort of research. But agents are not only used to conduct
research, one of their major tasks in the future will be
assisting human beings. This assistance can come in many
different forms, a robotic agent that cleans the house or a
software agent that helps you find a good vacation. It is very
important that humans can interact and communicate with
these agents. This means that the agents should understand the
human language, or at least a subset of a human language. For
example, when a human buys a robotic cleaning agent, he
does want to train the agent with literally thousands of
language games, so the agent can understand him. The agent
can also not be trained interacting with other agents, because
the language would not be understandable by humans, so it
would be very complicated to use an evolving language for
such a robot. Therefore a protocol would probably be much
more appropriate. Jacobus and Duffy have conducted more
research in this area [JD03]. They also base their ideas on an
ACL based protocol, which is extended with features from
human language, so the communication will be formal, but
also easy for a human being.
Interoperability is a large problem in both areas. When the
designer chooses to use a protocol, he has to decide first on an
ACL to use or to design its own. But this is not the only
choice; each ACL is only a very basic specification. A lot of
extra design has to be done and when the system has to
cooperate with other agents this can be very hard. In some
systems interoperability with humans is also a necessary
requirement. A designer should take this into account, but it is
also very possible that interaction with humans makes the
protocol design easier. Humans are very adaptable and will
adopt the language of the agents much faster than other
agents. This is even the case when evolving languages are
used, as can be seen in the Talking Heads experiment. Here
people were encouraged to try to interact with the robotic
agents and they managed to succeed. Both man and the
machine adapted their language in order to be able to adapt
[Ste99].
Verifiability is an important criterion. A system that is put
into use in a critical environment has to work correctly.
Although the correctness of an evolving language can be
checked using simulation, it is not guaranteed that this
simulation covers all possible problems. Even if it performs
correctly in the simulation, this does not have to mean it
works correctly in the real world, the simulation is after all
just a model of the reality. A protocol can be verified much
better, because of its finite nature. All states can be examined
and if the system works correctly for all states, the protocol is
correct.
There are several other properties mentioned, but none of
these can be directly compared. The advantage that evolving
languages are close to human language evolution is not
always a real advantage, but it is if simulating human
evolution and intelligence is the goal of the experiment. The
interpretation of how formal a specification should be
interpreted is still open for debate; this is already recognized
by [PM99].
4.2 Applications in domains
4.2.1 Predatory Prey
Predator prey is one of the best known domains where simple
concepts can easily be tested. The experiment consists of a
grid on which a prey is present in a certain square. There are a
number of predators, whose goal is to capture the prey by
locking it in on every side, so the prey cannot make any
move. There are many variations on this general idea (such
as whether diagonally moves are allowed and sometimes
predators may make two moves, whereas the prey may only
make one).
Jim and Giles conducted a research in this area [JG01]. Their
rules are very fair; the predators have the same capabilities as
the prey. They use an evolving language to let the agents
communicate. The communication is used to know which
agent is where en where the prey is. They do not have
individual knowledge of where the other predators are and
where the prey is, they only know what’s in the grid blocks
adjacent to them.
The performance measure used in this domain is usually the
number of steps it takes before the predators have enclosed
the prey. This performance depends heavily on the type of
prey. A random prey will much more likely caught (because
of its locality), a prey that walks continuously in one direction
is much harder to catch.
Jim and Giles claim their system outperforms regular
communicating systems, but do not back this up with
performance numbers of ordinary communicating systems.
The only claim they make is that the system outperforms all
previously designed predator prey systems. This claim
however, seems reliable and there is no obvious reason to
question their results.
4.2.2 Robot-soccer
Communication between different agents is not very widely
spread. However, in real life soccer, communication between
players is very important. In the research from Veloso, Stone
and Bowling [VSB98], communication is only minimally
used. It is only mentioned that it is possible, not how it is used
and whether it is important. More and improved
communication could result in better playing robot soccer
teams, especially evolving systems, because an evolving
system can constantly adapt itself to the situation. In soccer
games, new situations arise very often and an evolving system
can name these new situations and therefore cope with them
very well. Communication with a protocol could break apart
in such a situation. A downside of using an evolving language
is the time it takes to evolve it. The basic language can be
evolved using simulations (just like robot soccer agents are
trained now). The language that emerges from this simulation
can be used as the basis for the final language. This makes the
learning much faster than evolving an entire language when
playing real life games.
4.2.3 Other domains
There are various other widely known domains, but they
won’t be mentioned here. However, there are some
conclusions that can drown from the advantages and
disadvantages in general. Typically the type of
communication that is used in a system that incorporates other
systems from different designers is a communication protocol.
Such systems can be multi-agent systems on the internet that
cooperate with each other in order to achieve a certain goal.
An evolving language is almost not possible here; the only
solution is a well designed protocol. This protocol has to
incorporate all the necessary features, so it will be very
complex to design. Therefore, even using a protocol will not
ensure that the agents will be able to corporate, but it will be
more feasible than with an evolving language.
Also interacting with a human can be difficult. Here however,
are some more options. The Talking Heads Experiments
[Ste99] has shown that humans can communicate with the
agent with their evolving language, but it takes some effort on
the human side. Most likely people do not want to take effort
when the agent system is purely for their use, such as a
cleaning agent. Therefore communication with humans will
most likely use some sort of protocol, but not in the same way
as agents among themselves use.
5. CONCLUSIONS
There are a lot of differences between communication
protocols and evolving languages. The first is a very formal
and strict way of specifying communication, where all
communicative acts are predefined and no new acts are
devised during system use. Evolving languages however, are
flexible and are continuously adapted. A consequence of these
differences is the complexity of an evolving language can
grow very large, whereas the complexity of a communication
protocol is always the same.
Another result is that the research done in evolving languages
is very much research into the workings of the principle.
There are not many researches that try to apply the theories in
a system that has another purpose than to research evolving
languages. The Talking Heads Experiment is totally focussed
on the evolution of a language and the consequences of this. It
does not say anything about other application of the same
technique, such as improving the performance of multi-agents
systems.
Communication protocols have been around for longer time,
therefore a lot more research has been conducted in this area.
Also a lot of researches have been done in the specific
application of protocols and how it should be designed to
work best. But also with communication protocols, there are
not many researches with quantitative and comparable results.
Making a definitive conclusion is therefore very hard. In the
predator prey domain, the evolving language works very well
and it probably will work in comparable situations, such as
robot-soccer, where agents work among each other in order to
achieve a goal. However, when a multi-agent system has to
communicate with other systems, designed by others, using
evolving languages becomes very hard. Especially in
industrial applications, where most of all verifiability is very
important, evolving languages are not the best solution. In
those cases a communication protocol would be more
trustworthy, because of its verifiability and its transparency.
When an agent has to communicate with a human, a sort of
middle way would probably be the best solution. The basis
will be a communication protocol, because a human has to be
able to understand the agent, but this protocol should be
extendable and it should be able to learn from the human
being. A kind of middle way approach is also suggested in
[JD03].
6. FUTURE WORK
No real conclusive results could be drawn from the found
literature, a lot more research should be done in order to be
able to come up with conclusive results. Setting up an
experiment in which a direct comparison is made would shed
more light on when which technique can be used best. Several
simple experiments should be conducted in simple domains,
such as a direct comparison in predator prey. Another
interesting research would be using language evolution in
robot soccer. This could make is possible to make a better
performing soccer team, because they can communicate
simple tactical advise, about where a pass is going or what the
intentions are of a specific robot. Here one could compare
how different teams perform, without communication, with a
protocol communication and with an evolving communication
system. The results from such an experiment would be very
interesting.
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