Universidad Autónoma Metropolitana - MEXICO 1 MultiAgent Architecture and an Example.

Universidad Autónoma Metropolitana - MEXICO

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MultiAgent Architecture and an Example


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Ana Lilia Laureano-Cruces

e-mail : [email protected]

http://delfosis.uam.mx/~ana/AnaLilia.html

Universidad Autónoma Metropolitana – Azcapotzalco - 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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• 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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• 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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• 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 OUTPUT

Perception

Comunication

f


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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

MotivationResulting motivation tendency

Formation of intentions

Initiation of action

Action

Decision


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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

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 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

Data that constitutes the environment

WELLBORE DATA: Input data used by human-expert:

1. Temperature Logs 2. Temperature Simulated

the values of these variables remain constant in this case

3. Mass flow rate during lost of circulation 4. Porosity

Data that constitutes the autonomous agent


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Diagram of the data for the obtaining of existing temperatures

Repetitive process according to theparameters proposed quantitative

Existing

Existing Proprosal

{First time only}Logged

Existing Modified

SIMULATOR(virtual/environment)

Expert Decision

Repetitive process according to theparameters proposed quantitative

Existing Existing

Existing Proprosal

{First time only}Logged

Existing Modified

SIMULATOR(virtual/environment)

Expert Decision


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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 hotterEND_WHILE


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Dependency of agents

Adjustment

TempExist

Level 1

Action TempExist State

Goal Transfer

Agent View


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Logged (TReg) and simulated (TSim) temperatures for the test well. The resulting

formation temperatures (TMod) are also shown

0

100

200

300

400

500

600

0 500 1000 1500 2000 2500 3000 3500

Depth (m)

Tem

pera

ture

(C

) TMod

TSim

TReg


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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)


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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

Universidad Autónoma Metropolitana - MEXICO 1 MultiAgent Architecture and an Example.

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