Towards Automating the
Evolution of Linguistic Competence in Artificial Agents Using Negotiation :
The aim of our research is to understand and
automate the mechanisms by which language can emerge among artificial,
knowledge-based and
rational agents. Our ultimate goal is to design and implement agents
that, upon encountering other agent(s) with which they do not share
an agent communication language, are able to initiate creation of,
and further
able to evolve and enrich, a mutually understandable Agent Communication
Language (ACL).
First, the agents we are interested in are knowledge-based.
This means that they have a representation of facts about the world,
expressed as a set of sentences in some (hopefully well defined) Knowledge
Representation Language (KRL), for example first order logic, description
logic, Classic, KL-One, probabilistic logic, or similar. Our assumption
that
the agents have a preexisting knowledge base complements much of the
related work in artificial life and neural network based approaches
(see and
references in that volume), genetic algorithms based work, and recent
work in AI by Luc Steel.
Second, the agents are purposeful. Usually, this is taken
to mean that the agents have well defined goals, i.e., the precise
description of states of the world they are to bring about. The possibility that
agents may have different goals brings up the notion of self-interested
(or selfish) agents, which we allow. We further allow a more expressive
representation according to which an individual agent's purpose,
or preferences, are expressed in terms of a utility function, as postulated
by the utility theory.
Third, the agents are rational. This means that the agents
perform actions chosen so as to further their preferences, or goals, given what
they know. We follow the operationalization of rationality postulated
by decision theory, according to which a rational agent ranks actions
in terms of the expected utility of their results, and executes the
action with the highest expected utility.
We define Communication as the phenomenon of one
agent (speaker) producing a signal that, when responded to by another
agent (hearer), confers
some advantage (or the statistical probability of it) to the speaker.
This definition is supported by numerous approaches to study of communication
in cognitive science. Simply, the communicative act must be purposeful
and beneficial to the speaker, or else a rational speaker would not
bother to produce it. Using the the framework of decision theory, a
communicative act must lead to an increase of the speaker's assessment
of it's own expected utility. This approach allows one to treat communication
as action (see Austin's postulate in, since it is defined by its effects
on the state of knowledge of hearer and speaker.
In this work, we build on the work by Durfee,
Gmytrasiewicz and Noh on values of communicative acts, but we address
the issue of language
creation and evolution. Given that the ability to communicate can be
advantageous, the agents may want to enrich their communicative capabilities
if they are insufficient to begin with. Specifically, if two interacting
agents do not share a common Agent Communication Language (ACL), they
may want to initiate its creation and enrichment to allow mutually
beneficial communication. This is the driving force behind evolution
of linguistic
competence: Improving communication allows the agents to interact more
efficiently, and conveys an advantage which we measure as an increase
in the agents' expected utilities. This approach is different from
one taken by Luc Steels in which agents, playing a ``language game'',
are
directly rewarded for successful communication, rather than the reward
being assessed by the agents based on how communication helps them
solve a task at hand. As we mentioned, our employing the the knowledge-base
approach further sets our work apart from Steels' work, as well as
from
related research in artificial life and related fields.
We propose that initiation and enrichment of an agent communication
language can be accomplished by the mechanism of negotiation, developed
in the fields of economics and game theory, and automated in recent
work in artificial intelligence. In proposing negotiation as the main
component of our framework we are motivated by the process of language
development among humans coming from different linguistic backgrounds
that have to interact. Under such circumstances, people were found to
create a primitive language, called pidgin, further enrich it to more
syntactically sophisticated Creole. In this process, people are frequently
said to negotiate among themselves the lexicon and the rules of grammar
that become accepted as a part of a shared communication language.
We restrict our attention, to games that result
in unique equilibrium strategies and propose to use Kalyan Chatterjee
and Larry Samuelson's model of negotiation with incomplete information
on both sides with restricted offers. We will study the results of
words that arise under different conditions, when Value of Communicationto
both agents are private information known only to the agents themselves.
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