MultiAgent Architecture and an Example

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

2. Ana Lilia Laureano-Cruces e-mail : clc@correo.azc.uam.mx http://delfosis.uam.mx/~ana/AnaLilia.html Universidad Autónoma Metropolitana – Azcapotzalco - MEXICO Universidad Autónoma Metropolitana - MEXICO

3. Distributed Artificial Intelligence • Distributed resolution of problems • MultiAgent systems Universidad Autónoma Metropolitana - MEXICO

4. Distributed resolution of problems • Cooperating modules or nodes • The knowledge about the problem and the development of the solution is distributed Universidad Autónoma Metropolitana - MEXICO

5. MultiAgent Systems • Coordinated intelligent behaviour between a coordinated collection of autonomos agents: • Knowledge • Goals • Skills • Planning • Reasoning about the coordination between agents Universidad Autónoma Metropolitana - MEXICO

6. Contents • Basic ideas • Introduction (Control Theory and Cognitive Psychology) • MultiAgent Systems • An expert decision application • Conclusions Universidad Autónoma Metropolitana - MEXICO

7. 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. Universidad Autónoma Metropolitana - MEXICO

8. 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. Universidad Autónoma Metropolitana - MEXICO

9. 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. Universidad Autónoma Metropolitana - MEXICO

10. 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. Universidad Autónoma Metropolitana - MEXICO

11. 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. Universidad Autónoma Metropolitana - MEXICO

12. 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. Universidad Autónoma Metropolitana - MEXICO

13. 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 Universidad Autónoma Metropolitana - MEXICO

14. 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. Universidad Autónoma Metropolitana - MEXICO

15. The origins Control Theory Vs. Cognitive psychology Theory Control Cognitive Psychology Classical AI Planning systems Universidad Autónoma Metropolitana - MEXICO

16. 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. Universidad Autónoma Metropolitana - MEXICO

17. 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 Universidad Autónoma Metropolitana - MEXICO

18. A black box agent model OUTPUT INPUT f Perception Comunication Universidad Autónoma Metropolitana - MEXICO

19. An agent is internally described through a function ‘ f ’ • fis 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. Universidad Autónoma Metropolitana - MEXICO

20. 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 functionf which determines the agent´s behaviour. Universidad Autónoma Metropolitana - MEXICO

21. Cognitive Psychology • Control theory investigates theagent-worldrelationship from amachineoriented 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 ofcognitive psychology, particularly ofmotivation theory. Universidad Autónoma Metropolitana - MEXICO

22. From Motivation to Action Resulting motivation tendency Motivation Formation of intentions Decision Initiation of action Action Universidad Autónoma Metropolitana - MEXICO

23. Motivational Theory • The motivation theory study is centered around the problem of finding out whyan agent performsa certain actionorreveals a certain behaviour. This covers the transition from motivation to action; where two subprocesses that define two basic directions in motivation theory are involved. Universidad Autónoma Metropolitana - MEXICO

24. Formation ofintentions: 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 toPlatónandAristóteles. They defined it along 3 categories: cognition, emotion and motivation. Universidad Autónoma Metropolitana - MEXICO

25. 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. Universidad Autónoma Metropolitana - MEXICO

26. Darwin shifted the focus of motivation research from a person-centered to a situation-centredperspective. • He established a duality between thehuman and animal behaviors. • As a consequence, it was found that many of the models corresponding to animal behavior are also valid for humans. Universidad Autónoma Metropolitana - MEXICO

27. Another consequence of Darwin’s theory is that human intelligence was viewedas 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. Universidad Autónoma Metropolitana - MEXICO

28. 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). Universidad Autónoma Metropolitana - MEXICO

29. 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. Universidad Autónoma Metropolitana - MEXICO

30. Dynamic Theory of Action • For every point in time tand for each behavioral tendencyb; the theory determines a resultant action a tendency. • That is, how strong isb at time t. • The maximal tendency is called dominating action a tendency at time t. Universidad Autónoma Metropolitana - MEXICO

31. The input for a DTA are an instanttin 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. Universidad Autónoma Metropolitana - MEXICO

32. The output of the DTA is the resulting tendency of action fora and tnwhich is computed as a function of the four forces defined above. • This work is related withMaes Theory (agents can have goals), with the BDI architecture, and with the control selection of the exhibit mechanism of the pedagogical agents behaviors. Universidad Autónoma Metropolitana - MEXICO

33. 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? Universidad Autónoma Metropolitana - MEXICO

34. 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. Universidad Autónoma Metropolitana - MEXICO

35. 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. Universidad Autónoma Metropolitana - MEXICO

36. 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 Universidad Autónoma Metropolitana - MEXICO

37. 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 Universidad Autónoma Metropolitana - MEXICO

38. 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. Universidad Autónoma Metropolitana - MEXICO

39. 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. Universidad Autónoma Metropolitana - MEXICO

40. 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. Universidad Autónoma Metropolitana - MEXICO

41. An instigator is a force that pushes the action tendency forb at timet. • A consummatoris used to weaken the instigating force for bover 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 timet. • A resistant force weakens the inhibitory force over time. Universidad Autónoma Metropolitana - MEXICO

42. 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. Universidad Autónoma Metropolitana - MEXICO

43. However exploration and develpoment activities are focused on use of geothermal resources. • The Universities and the CFE (Comisión Federal de Elecricidad) Universidad Autónoma Metropolitana - MEXICO

44. 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 Universidad Autónoma Metropolitana - MEXICO

45. 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. Universidad Autónoma Metropolitana - MEXICO

46. 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. Universidad Autónoma Metropolitana - MEXICO

47. From 1990 to 1994 detailied geological studies were made in the following geothermal zones: • Las tres vírgenes (Baja California Sur): • Hidrology • Tectonics • stratigraphy • volcanology Universidad Autónoma Metropolitana - MEXICO

48. El Ceboruco-San Pedro (Nayarit) • Hidrology • Tectonics • volcanology Universidad Autónoma Metropolitana - MEXICO

49. Geothermal Fileds and Geothermal zones under exploration in Mexico Universidad Autónoma Metropolitana - MEXICO

50. 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. Universidad Autónoma Metropolitana - MEXICO