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MultiAgent Architecture and
an Example
Universidad Autónoma Metropolitana
- MEXICO
1
Ana Lilia Laureano-Cruces
e-mail : [email protected]
http://delfosis.uam.mx/~ana/AnaLilia.html
Universidad Autónoma Metropolitana –
Azcapotzalco - MEXICO
Universidad Autónoma Metropolitana
- MEXICO
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Distributed Artificial Intelligence
• Distributed resolution of problems
• MultiAgent systems
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Distributed resolution of problems
• Cooperating modules or nodes
• The knowledge about the problem and
the development of the solution is
distributed
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MultiAgent Systems
• Coordinated intelligent behaviour between a
coordinated collection of autonomos agents:
•
•
•
•
Knowledge
Goals
Skills
Planning
• Reasoning about the coordination between
agents
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Contents
• Basic ideas
• Introduction (Control Theory and
Cognitive Psychology)
• MultiAgent Systems
• An expert decision application
• Conclusions
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Basic Ideas
• The intelligence of the majority of traditional
problem solving algorithms is incoporated by
the designer.
• As a result, they are predictable and do not
allow for unexpected results.
• This type of systems are repetitive, and
always yield the same output for a given set
of input data.
• Modifying these codes is normally a very
complicated task.
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Basic Ideas
• The resolution methods based on the
association of agents are conceived to exhibit
emergent behavior rather than a predicatble
one.
• It is possible to create new agents to take
care of situations that are not taken into
consideration during the original design,
without the need of modifying existing
agents.
• The basic idea is to conceive the solution as
a set of restrictions to be satisfied rather than
as the result of a search process.
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Basic Ideas
• By creating a society of agents, it is possible that
each one of them is in charge of a subset of
restrictions.
• In this manner, the global problem is solved through a
series of negotiations or intervention hierarchy
between agents, rather than through searching.
• Each agent could represent different interest
conflicts, which should be followed carefully.
• If at the end of the iteration an adequate solution is
not reached, a restriction has not been taken into
account, and an agent that considers it should be
introduced.
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The nature of AI problems
• There are two classes of AI problems.
• Classic problems (related with
optimization).
• Everyday problems of human beings.
• The central idea is to find a solution
that, without being optimum, satisfies
our requirements.
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When we think in MultiAgent Systems to solve the
problem we most take into account some ideas ...
• In spite of its complexity, any problem can
be decomposed in tractable parts.
• The relationship between its parts is weak,
that is, an increasing complexity does not
affect the interaction between them.
• The specifications of the problem and the
control is distributed among all the
agents.
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When we think in MultiAgent Systems to solve the
problem we most take into account some ideas ...
• An individual agent is not interested in the global
problem it is solving.
• The result of the interaction of agents provides the
solution that is being searched.
• This perspective is that of distributed AI.
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When we think in MultiAgent Systems to solve the
problem we most take into account some ideas ...
• What is the difference between the classical
and agent strategies?
• S = (p1,p2,...pk).
• S = p1 x p2 x ... x pk
• S = p1 x p2
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When we think in MultiAgent Systems to solve the
problem we most take into account some ideas ...
• The problem is distributed.
• Each agent represents a relevant entity
for the problem to be solved, and has
an individual behavior.
• When interacting between them and
their environment, each agent follows
its own strategy.
• Within this context, solutions emerge.
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The origins
Control Theory Vs. Cognitive
psychology
 Theory Control
 Cognitive Psychology
 Classical AI Planning systems
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Philosophical roots
• Origins in the 18th century.
• Foundation of model control theory laid by
James Watt.
• Mechanical feedback to control steam engines.
• Cybernetics tried to unify the phenomena of
control and communication observed in
animals and machines into a common
mathematical model.
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Agents
• This term is used to characterize, starting
from primitive biological systems, very
different kinds of systems.
• Biological: ants, bees.
• Movil Robots and air planes.
• Systems that simulate or describe whole human
societies or organizations such as:
• shiping companies
• industrial enterprises
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A black box agent model
INPUT
Perception
OUTPUT
f
Comunication
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An agent is internally described
through a function ‘ f ’
• f is a function which takes perception
and received messages as input and
generates output in terms of performing
actions and sending messages.
• The mapping f itself is not directly
controlled by an external authority: the
agent is autonomous.
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This general view of an agent allows
its modelling through:
• Biological models
• Based-kowledge models (this kind of models
can be defined by mental states)
• What makes this models drastically different
is :
• the nature of the function f which determines
the agent´s behaviour.
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Cognitive Psychology
• Control theory investigates the agent-world
relationship from a machine oriented
perspective.
• The question of how goals and intentions of a
human agent emerge and how they finally
lead to the execution of actions that change
the state of the world, is the subject of
cognitive psychology, particularly of
motivation theory.
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From Motivation to Action
Motivation
Resulting
motivation
tendency
Formation of
intentions
Decision
Initiation of
action
Action
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Motivational Theory
• The motivation theory study is centered
around the problem of finding out why
an agent performs a certain action or
reveals a certain behaviour. This covers
the transition from motivation to action;
where two subprocesses that define two
basic directions in motivation theory are
involved.
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• Formation of intentions: how intentions are
generated from a set of latent motivation
tendencies.
• Volition and action; how the actions of a
person emerge from its intentions.
• The investigation of reasons, motivations,
activation, control and duration of human
behavior goes back at least to Platón and
Aristóteles. They defined it along 3 categories:
cognition, emotion and motivation.
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• The main determinant of motivation was
situated in the human personality: a human
being is a rational creature with a free will.
• In AI, the human needs and goals have been
structured in a hierarchical way.
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• Darwin shifted the focus of motivation
research from a person-centered to a
situation-centred perspective.
• He established a duality between the human
and animal behaviors.
• As a consequence, it was found that many of
the models corresponding to animal behavior
are also valid for humans.
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• Another consequence of Darwin’s
theory is that human intelligence was
viewed as a product of evolution rather
than a fundamental quality which is
given to humans exclusively by some
higher authority.
• Thus, intelligence and learning became
a subject of sytematic and empirical
research.
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• In the case of AI, hybrid architectures
have been develpoed to combine both
paradigms (person-centred and
situation-centred).
• Dynamic theory of action (DTA). (Kurt
Lewin 1890-1947).
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Dynamic Theory of Action
• It is a model explaining the dynamics of
change of motivation over time.
• The model starts from a set of
behavioral tendencies which can be
compared to the possible goals of a
person.
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Dynamic Theory of Action
• For every point in time t and for each
behavioral tendency b; the theory
determines a resultant action a
tendency.
• That is, how strong is b at time t.
• The maximal tendency is called
dominating action a tendency at time t.
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• The input for a DTA are an instant t in the stream of
behavior, and an action tendency which is given by a:
• motive (person-centered)
• an incentive (situation-centered)
• The dynamics of a DTA is described by means of four
basic forces:
•
•
•
•
instigator
consummator
inhibitor
Resistant force.
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• The output of the DTA is the resulting
tendency of action for a and tn which is
computed as a function of the four forces
defined above.
• This work is related with Maes Theory
(agents can have goals), with the BDI
architecture, and with the control selection of
the exhibit mechanism of the pedagogical
agents behaviors.
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From the point of view of a
computer scientist ...
• How can motives and situations be
represented and recognized?
• How can the influence of motives and
situations to the basic forces: In, Co, Ini, and
Re, be put into a computational model.
• Can we reduce an agent to a finite set of
potential behavioral tendencies?
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Clasical AI Planning systems
• The planning systems are seen as:
• a world state
• a goal state and
• a set of operators
• Planning can be looked as a search in a
state space, and the execution of a plan
will result in some goal of the agent
being achieved.
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The analogy with the agents
theory
• The agent has a symbolic representation of
the world.
• The state of the world is described by a set of
propositions that are valid in the world.
• The action effect of the agent in the
environment are also described by a set of
operators, and the resulting world state.
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Reactive-Agents Architectures
• The design of these architectures is
strongly influenced by behavioral
psychology.
• Brooks, Chapman and Agree, Kelabling,
Maes, Ferber, Arkin
• These kind of agents are kown as:
• behaviour-based
• situated or
• reactive
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Reactive Agents
• The selection-action dynamics for this type of
system will emerge in response to two basic
aspects:
• the conditions of the environment
• internal objectives of each agent
• Their main characteristics are:
• dynamic interaction with the environment
• internal mechanisms that allow working with
limited resources and incomplete information
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• The design of reactive architectures is
partially guided by Simon’s hypothesis:
• the complexity of an agent’s behavior
can be a reflection of its opertating
environment rather than of a complex
design.
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• Brooks thinks that the model of the world is
the best model for reasoning
• ... and to build reactive systems based on
perception and action (essence of
intelligence)
• Once the essences of being and reaction are
available, the solutions to the problems of:
behavior, language, expert knowledge and its
application, and reasoning, become simple.
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Functionality Vs. Behavior
• From a functional perspective, classical AI
views an intelligent system as a set of
independent information processors.
• The subsumption architecture provides an
oriented descomposition of the activity; in this
way a set of activity (behaviors) producers
can be identified.
• The behaviors work in parallel, and are tied to
the real world through perceptions and
actions.
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• An instigator is a force that pushes the action
tendency for b at time t.
• A consummator is used to weaken the instigating
force for b over time. This force is only active while
the behavioral tendency b is active.
• An inhibitor is a force which inhibits the action
tendency for b at time t.
• A resistant force weakens the inhibitory force over
time.
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Present situation of Geothermics
in Mexico
• Up to present geothermal resources in
Mexico are utlized to produce electrical
energy
• Some geothermal resources are utlized for
different purposes:
• Turist
• Therapeutic
• Use of the separated waters or the waste heat for
industrial in mexican geothermal fields.
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• However exploration and develpoment
activities are focused on use of
geothermal resources.
• The Universities and the CFE (Comisión
Federal de Elecricidad)
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• Regional Geothermal assessment in
Mexico was completed 1987:
• When 92% of the whole territory had been
covered
• The remining 8% has no geothermal
because of its tectonically stable location
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By 1987 ...
• 545 thermal localities had been
identified, which grouped around 1380
individual hot points including:
• Hot springs
• Hot water shallow wells
• Hot soils
• Fumaroles, etc.
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• By 1990, 42 geothermal zones has
been located
• In those zones, pre – feasabilty studies
(geology, fluid geochemistry and
geophhysics) had been conduced in
varynig stages.
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• From 1990 to 1994 detailied geological
studies were made in the following
geothermal zones:
• Las tres vírgenes (Baja California Sur):
•
•
•
•
Hidrology
Tectonics
stratigraphy
volcanology
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• El Ceboruco-San Pedro (Nayarit)
• Hidrology
• Tectonics
• volcanology
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Geothermal Fileds and Geothermal
zones under exploration in Mexico
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Drilling Activities
• Currently there are 68 geothermal wells,
representing 104, 859 drilled meters.
• E.g. In the Humeros Geothermal field two
deep wells were drilled
• There are in Mexico, up to the present,
356 deep wells drilled for electrical use
of geothermal resources. These wells
give a total amount of 715,090 drilled
meters.
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• Currently Mexico has an installed
geothermal electric capacity of 753 Mwe
• It represents 7% of the overall country
production
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An example
• One of the objectives of artificial intelligence
refers to the development of systems that
ease or increase the level of comfort in the
daily life of humans. Such is the case for
tasks with permanent focus on the input data
in convergent methods or systems that help
in the decision-making process involved in
costly processes.
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An example
• In this example we propose a design’s
of the expert’s decision – making
process trough the use of a cognitive
model, and fuzzy sets to model the
agents’ reactive deliberative process.
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• Software system helps human expert in
the estimation of the static formation
temperatures.
• Furthermore, we will present an
example based on a behavior
developed from an expert in the field of
geothermal sciences.
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• An attempt to estimate formation
temperatures from logged temperatures
was solved whit this methodology based
on reactive decision model.
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Adaptative Behavior
• Autonomy is also known as adaptive
behavior and it has the capacity to
adjust itself to the environment
conditions
• It is the essence of the intelligence and
it is the animal ability to fight
continuously against the world;
complex, dynamic and unpredictable.
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• This ability is seeing in terms of
flexibility to adjust the behavior
compendium to the contingencies
anytime as a product of the interaction
with the environment.
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When we use agents to simulate
an adaptative behavior
• Agents can be developed from two
perspectives:
• knowledge and automatic learning acquisition
• the domain expertise is codified from a human
expert
• In our study case we design the adaptative
behavior taken into account the human
expertise
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the design of the representation
of dynamical environment
• could be from two approaches:
• the traditional AI considered that the
success of an intelligent system is closely
related with the degree of the domain
problem, which can be treated as a
microworld abstraction (symbolic
processing approaches), that is, at the
same time, disconnected of the real world.
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• There exists another group whose design
is usally bottom - up, it is an etologic
design and bears in mind the fundamental
steps of animal behavior (subsymbolic).
These approaches also empathizes
symbol grounding where various behavior
modules of an agent interact with the
environment to produce complex behavior.
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• However this group concedes that achieving
human-level artificial intelligence might require
integration of the two approaches.
• In our study case, referring to a simulator control,
the behavior agent has to be connected to the
simulator,
which
represents
a
dynamic
environment, modelling the domain expertise to
the adaptive process. In this case it represents a
symbolic grounding representation
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Agents
• Agents continuously perform three
functions:
• perceptions of the dynamic conditions from
the environment
• actions that can change the environment
conditions
• reasoning for interpreting perceptions,
solving problems, making inferences and
taking an action
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Agents
• Conceptually perception inputs data for
the reasoning process and the
reasoning process guides the action
• In some cases the perception can guide
the action directly
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• One of the problems in the design of these agents is
to establish a decision-making process with
subjective domains:
• Natural environments exhibit a great deal of structure that a
properly designed agent can depend upon and even actively
exploit
• Strictly talking about the things required to achieve an
adaptive behavior, a structural congruence between the
internal dynamic mechanisms of an agent and the external
environment dynamic is needed
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• As long as this compatibility exists, both
the environment and the unit act as
mutual sources of disturbance, release
and conditions alteration
• In this case it is a two non-autonomous
dynamical systems
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• The agent (the human – expert) and the
environment (the simulator). The design
of these systems can be seen as a
control problem.
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A control problem:
• have two sub-problems:
• the state estimation, consisting in the
evaluation of the environment (perception)
and the controller’s input.
• regulation, consisting in finding an
adequate response to the environment
state (action)
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The controller consists of:
• a function (f) that estimates the
environment’s state
• a function that regulates the
environment’s response
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From the perspective of AI
• the agent has the ability to recognize
certain class of situations, which derive
in objectives and thus, develop actions
that lead to the achievement of these
objectives
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• Most of the environments are too complex to
be described by differential equations
• The behavior of a shipment company of an
airport, or cognitive processes involving
expertise, need a kind of symbolic model
• The classic control theory can not deal with
incomplete information regarding the
environment in a successful way
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• In the case of agents, heuristics are
use.
• Its use implies a basic difference
because the f function can be
implemented through differential
equations or symbolic reasoning
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• A model having an agent and its
environment imply the existence of two
dynamic systems having convergent
dynamics; that is, the value of their state
variables do not diverge to infinity, but
eventually converge to a limit set
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• Figure 1 shows the dynamical systems and
the variables of our study case. The
WELLBORE DATA is included in the symbolic
model and these variables will make the
human-expert (autonomous agent) reason. In
this example the input data used by the
human-expert of some variables remain
constant (the mass flow rate during lost of
circulation and porosity).
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Dynamic Systems that constitutes the
environment and the autonomous agent
Data that constitutes the environment
Data that constitutes the autonomous agent
WELLBORE DATA:
Input data used by simulator:
WELLBORE GEOMETRY:
Well bore section
Well bore diameter
Well bore depth
Axial nodes
Drill pipe diameter
and thickness
THERMOPHYSICAL AND
TRANSPORT PROPERTIES:
FORMATION, CEMENT,
CASING AND DRILLING
FLUID.
Thermal conductivity
Specific heat capacity
density
and viscosity to drilling fluid
Input data used by human-expert:
1.
2.
Temperature Logs
Temperature Simulated
the values of these variables remain constant in
this case
3.
4.
Mass flow rate during lost of circulation
Porosity
FLOW AND TEMEPRATURE
DATA OF THE WELL
DRILLING OPERATIONS
Fluid flow rate
Geothermal gradient (Initial
condition)
Surface temperature
Inlet fluid temperature
WELLBORE DATA:
Temperature Logs
Temperature Simulated
Mass flow rate during lost of
circulation
Porosity
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Diagram of the data for the obtaining of
existing temperatures
Existing Proprosal
{First time only}
Logged
Repetitive process according to the
parameters proposed quantitative
SIMULATOR
(virtual/environment)
Existing
Modified
Expert Decision
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Existing
75
Mental model of experts decision
WHILE | TSim – TReg | > 5º DO
IF TSim > TReg (it implies that the temperature was assumed hotter than
actually is)
THEN
Adjust the existing temperature colder
ELSE (TSim < TReg; it implies that the temperature was assumed colder
than it actually is)
Adjust existing temperature hotter
END_WHILE
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Dependency of agents
Adjustment
Action TempExist
State
Level 1
Goal Transfer
TempExist
Agent View
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Logged (TReg) and simulated (TSim)
temperatures for the test well. The resulting
formation temperatures (TMod) are also shown
Temperature (C)
600
500
TMod
TSim
TReg
400
300
200
100
0
0
500
1000
1500
2000
2500
3000
3500
Depth (m)
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Conclusions
• Due to its usefulness and full applicability
many areas of computer science have rapidly
adopted this sample and powerful concept
• On AI the introduction of agents is partially
due to the final deficulties when we try to
solve problems considering the features of
the external world or when the agent is
involved in a problem solving process
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Conclusions
• The solutions to address these problems can
be limited and inflexible if there is not a good
perception of the external world features.
• As a response to this difficulty, the agents
receive inputs from the environment through
devices that allow them to perceive the world.
• In response to these inputs, they develop
actions causing effects on the environment.
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Conclusions
• In our example we were established two
agents:
• An autonomous
• Non-autonomous
• This implies a distributed solution to the
problem, which consists of finding the existing
temperatures.
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Conclusions
• These characteristics provide the properties
of robustness and answer quality to the
system.
• The basic reactive behavior design of the
agent was carried out through located activity
that is focused on the agent’s actions and,
therefore, on its basic behaviors according to
the situation, moments and environments.
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Conclusions
• It is fundamental to find the specific
perceptions that will cause a certain action on
a present environment.
• To achieve this, a cognitive model that
represents the expert’s decision, was
developed.
• This model allows the consideration of the
different situations that can occur in the
environment, to achieve an emergent
response of the system.
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Conclusions
• The behavior has been formalized taking into
account all the control variables of the
process:
• a) goal type,
• b) knowledge type and
• c) perception and action of each agent.
• This formalization provides an interaction
between agents with a well-defined interface
that guarantee a congruent behavior of the
muti-agent system (environment-agents or
agents-agents) Universidad Autónoma
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Conclusions
• The temperature behavior in the geothermal
well has been successfully modeled since
the difference between simulated and
logged temperatures is inside the human
perception.
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Conclusions
• Finally, this work is an example of a
design technique proposed for the
development of multi-agent systems
with reactive characteristics, which
shows the simplicity (with respect to
previous works) that has been achieved
through the development of the
software that controls a dynamic
process that involves many variables
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