[EUMAS10] J-MADeM v1.0: A full-fledge AgentSpeak(L) multimodal social decision library in Jason

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by Francisco Grimaldo

Trying to produce social intelligent agents that shows an acceptable behaviour in social envinronments. Applied to BDI agents and using an auction model as decision/making mechanism. It seems interesting for us, as the last step for reaching a concrete agreement after an agreement space has been created using a consensus network. And it is implemented over Jason, so we can integrate it in Mgx agents.

The API seems to extend a Jason agent with predicates that can be introducced in the rules. So if we get a network of jason-mgx agents, we can program agents with decision making procedires that maximizes the benefit of a concrete water rights distribution among participants.

An interesting work that can be useful for us. I'll read the paper later

[AT workshop] Session 4

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Reputation and confidence for artificial intelligent entities. A cognitive approach
(Jordi Sabater)

Trust deals with uncertainly and risky situations. A little difference: reputation (very similar) is one of hte mechanism to build trust and it is a social element. How it is used in a computer-based systems? Three layers (approaches): security, institutional and social. Trust and reputation are meaningful in the social approach. If we have a storngly ruled system (institutional approach) we do't need trust, just to follow the rules. Then, a cognitive model of reputation is needed.

A social evaluation is the evaluation by a social entity of some property (mental, physical or social) related with been social. Reputation is then a voice (something that is said) about a social property. But agents do not have to beleave this reputation measures: agents (as people) has no responsibility about spreading social evaluations. When people believes what other people sais, then reputation matches with image (what an agent believes in, consideres as true facts).Reputation means communication and gossiping is the channel used to transmit reputation measures. Images and reputation are based on facts, which have two measures: value and strength -> repage mechanism.

This repage cognitive computational model has to be inserted in an agent. It is important that (i) reputation model can be isolated from other reasoning mechanisms (planners, decision making tools); and (ii) be proactive: do not wait to be asked about reputation, but provide information to the rest of elements. Using a BDI (beliefs, desires and intentions) model with multicotext logics and bridge rules to integrate the context of teh repage mechanism into the context of beliefs, desires and intentions. In the logic, the difference between images and reputation is a ¿reified? difference. An argumentation model is used

Psychopharmacology of agreement
(Adolf Tobeña)

There's lots of corrdination, obbidion, ... but few agreements among humans. ANd the second point of the speech is that humans need drugs. And these facts "llevan" to psychiatric aspects of agreement: why patients are more trending to cooperate/agree after been treated?

Usually, xanthines (caffeine, tobacco) are present during negotiations and bargaining processes. 5 years ago was demostrated that oxitocin increases trust in humas. Furthermore, they observed that participants trend to not change the trusting behaviour even after knowning they had been betrayed (50% trials) and the brain was actually don't responding as been betrayed (e.g. activity in brain areas related with dissgust).

booster drugs for agreements (prosocial, protrust)

  • alcohol, cannabinoids
  • xanthines, nicotine
  • oxytocine, prolactine, NPY
  • estrogens

and antiagreement drugs are (indice paranidogenic, autistic and antisocial behaviors)

  • cocaine, amphetamines
  • LSD, mescaline, psilocibine
  • androgens

But they've observe that testosterone had a possitive effect on human bargaining behavior.... and they did it on women!!!! They shown that one sunlingual dose os testosterone in women cause a substantial increasein fair bargaining, reducing cinflics and increasing efficiency on social interactions. ANd usinga placebo they demonstrate that was a real effect (the believed testosterone group behaves as the group without testosterone. And in men? Other group showed that high levels of testosterone (natural measuring) reject low (unfair) ultimatum game offers: $5/$40. Testosterone has influence in how the rest of the people consider others as leaders. Testosterone redcuces conciuos detrection of signals (face expressions) serving social correlations ->  a high probability of entering into a fight is related with risk/venturesome behavior (you accept more faces as neutral)

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[AT workshop] Session 3

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The neural basis of empathy and coordination
(Christian Keysers)

1.- feeling the intentions of others
The neurons involved in a concrete movement (grasping something), surprisingly, respond also when the action is seen (about the 10% of the neurons - mirror neurons-).  Interesting: you can "run simulations" in your mind and the brain behaves as if the real action is being performed. But, what happened if you see a not human (f.i. a robot) doing the same action?. The active areas in the brain of the observer are the same. That is, your brain is "learning" how to do this action.

How about sounds?. The set of neurons dedicated to do, see or hear something is different. In humans, experiments done where about to hear the result of actions performed by the hands or by the mouth (clearly separated in the brain). The correspondent motor areas are not activated, but the area that responds to the stimuli does.

SO, how do we coordinate each other? Because the coordinate system of the other doing an action is not our own coordinate system and the active area in the brain is different. The mirror system transform back and forth between sensory an motor representations, providing the basis for optimal coordination of observed and executed actions

2.- why do we cooperate?
It is related with emotional behavior. Experiments done with pleasant and disgusting smells. Again, the response of the brain is very similar when we feels disgust or when we see someone felling disgusted (by their expression in the face) And impairing simulation with real stimuli can damage the brain (so we cannot properly distinguish the correct emotion/sensation). Emotional simulation and empathy are linked too? It seems to be, and it is not exclusive for disgust. Pain in self and in others overlaps, but disgust and joy overlaps too, so it is difficult to identify the correct emotion. Any way, this facts motivate us to cooperate: we share the same things than others (empathy).

Cooperation and generosity
(Paul van Lange)

Generosity: behaving more cooperatively than the others. Noise refers to unintended errors that affect interaction outcomes. Noise is a matter of fact in social systems and undermines cooperation. But generosity can (or not) cope with noise.

To understand social situations one needs to understand dependence, interests and information availability (al least).imperfect information appears in partner preferences or discrepancies about outcomes and intentions (why he's not responding my emails?).

But the amount of generosity to apply has to be biased. The optimal balance between reciprocity, generosity o stingy has to be found (e.g. tit-for-tat: nice, forgiving, retaliatory and clear.... but it does not repair)

After a lot of results, seems that, under negative noise, generosity (i) build trust, (ii) pair well with reciprocity, and (iii) -I missed this one-. Besides: communication helps (when noise happens, inform the other -say sorry-); individuals copes with noise better than representatives and empathy is effective.

NOTA: ¿que ocurre si se introduce la generosidad como un factor  más en el demostrador mWater a la hora de gestionar las agrupaciones de usuarios autoorganizadas? Parece que puede ser una buena variable para mantener una gestión óptima en el problema de los comunes.

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[AT workshop] Session 2

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On the use of argumentation in agreement technologies
(Henry Prakken)

Agents need argumentation (i) for their internal reasoning and (ii) for their interaction with other agents. Explaining basic things about argumentation process: argument attacks. The situation of the dialog can be modeled in a graph colored by defining in and out arguments (Dung, 1995). And there is a sound and complete game that allows to determine if an argument is feasible or not without having to calculate the entire network: an argument A is feasible when there is a winning strategy for A follow.ing the game rules.

Problem: it is asumed that all information is centralized and static (a single theory -KB-) So dialogue game systems are developed. He's using the Walton & Kreebe dialogue types (without eristic :-) I've seen this a lot of times already.

An Interesting thing: blocking behavior (always asking why) It can be solve by using sanctions:
social sanctions (i wont talk you any more)
shift of burden of proof by a third party (referee): q since r // why r? // referee: you must defend not-r

I already knew most of these things (thanks to Stella)

"Prof. Kripke, let me introduce Prof. Nash", or
Logic for Automated Mechanism Design
(Mike Wooldridge)

In MAS the interaction is done by mechanisms = protocol + self-interest and agents are the participants in these mechanisms. So mech. can't be treated as simple protocols. (ex. sniping in eBay -bidding in the last 5 min. trying to be the last bidder-). A MAS can predict the sniping behav. of users in eBay?  The environment of an agent is a mechanism too, that contains other agents that act strategically to achieve their own goals

The formalization used is ATL (alternate-time logic), introduced in 1997 top analyze games. It defines a branching-time model as a graph and CTL is the logic used to talk about branching-time structures, extending propositional logic with path quantifiers (A,E) and tense modalities (F, G, X, U).

CTL sais when something is inevitable or possible, but it hasn't notion of strategy action nor agency (it's a problem to model mechanisms.... and service-based applications too). ATL is intended to overcome these limitations. The basic expression is

$latex \langle \langle C \rangle \rangle \phi$

meaning "coalition C can cooperate to ensure that $latex \phi$. The idea is that, using coalitions, we can model who is going to achieve a property (a coalition can be an individual entity or even an empty set -modeling 'nature'-). An example about social choice (voting) mechanism. Now, mechanisms can be validated.The logic can capture dependencies among agents, as stressfulness (all goals met), veto (j needs i to achieve its goal), mutual dependence (all agents are mutual dependence... veto relationship)

(note: but we can't model actions yet, so I guess it isn't useful for us)

A concrete application about social laws (normative systems). Objectives will be ATL formulae $latex phi$ and mechanisms are behavioral constraints $latex \beta$ To avoid undesirable behaviors, we have to cut out some transitions. An effective social law $latex (\phi,\beta) \models \phi$. But compute this is a NP-hard problem. An example with the typical train organization in a tunnel. But you cannot model just the properties you want to avoid. The properties you want to preserve have to be modeled too in order to have system doing useful things.

But, what to do with non-compliance? The idea isto incentive compatibility and, to do this, we need preferences (a prioritized list of goal formulae). I like this idea: the utility of the agent comes from this list, from a worst (and weak) rule to the best (and stronger) rule. For instance, related with resources, have it assigned often and for a long time.

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[AT Workshop] Session 1

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Towards the biological basis of cooperation
(Arcadi Navarro)

Talking about genome and human evolution. The interesting thing: the effects on social behavior.

After a very interesting introduction to genomic, begins trying to relate genetics with social behavior: because to cooperate can have some explanations in our genes (and this can be the explanation of why humans have  been a successful specie): genetic variability for behavioral traits is considerable. The problem is that this is very difficult to interpret. Fortunately, there are some genetics related with economic behavior that can be studied and replicated in labs.

Example: the ultimate game: people trend to make 50:50 offers and to reject less that 30% (not an reasonable decision from an economic point of view). But chimpanzees behaves as rational maximizes in an ultimatum game. Both species have evolve completely different behaviors. Why? we have to study this from a genetic perspective. -> agents playing games are as chimpanzees. And researchers are discovered that serotonin makes individuals to be more generous (just a joke: men have more serotonin than women). Or even between MZ twins, differences in the acceptance threshold in ultimatum game have been observed. Examples with more genes.

Measuring Strategic Uncertainly and Risk in Coordination-, entry-Games and lotteries with fMRI
(Rosemari Nagel)

Uncertainty can be classified as

  • exogenous (risk): know the prob. of all possible states of the world (objective prob.)
  • endogenous: in absence of endogenously given prob.;  -> strategic uncertainty (SU) e.g. outcomes depends on social interaction -games- (subjective prob.)

How brain solve individual or strategic uncertainty? Can we predict choices and brain activity in games?
Results: people behaves similarly in lottery and coordination games, but not in entry games. And the activity in the brain increases in lottery -> coordination -> entry. Some graphics about the different parts of the brain active while playing each type of game. Similar activity in entry games of risk lovers and risk averse people.

Summarizing, the entry games create mode strategic uncertainty as predicted by the nature of the mixed equilibrium which also involves levels of reasoning.

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mWater: a case study of AT

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mWater es el demostrador que tenemos asignado en el AT. Se trata de ver cómo usar los conceptos de acuerdos en un caso de gestión eficiente de agua.

Se aplica sobre un entorno en concreto (acuífero La Mancha Oriental), donde se identifica además la estructura de distribución del agua y la estructura social existente. La implementación se realiza sobre instituciones electrónicas (primer prototipo) y Thomas (sistemas abiertos). Actualmente se está desarrollando en mercado intra-cuenca para la gestión de derechos de agua modelada con Islander.

Básicamente, el proceso puede resumirse de la siguiente manera

  1. cuando los agentes entran en el mercado, deben registrarse en las escenas iniciales
  2. a continuación, pasan a un trading hall en el que pueden ver qué transacciones se están realizando
  3. las transacciones se realizan en un conjunto de trading tables. El agente selecciona en qué mesa(s) quiere participar.
  4. en las mesas se firman contratos entre partes donde se negocian derechos de agua
  5. los contratos deben quedar expuestos 30 días para que otros agentes afectados puedan efecturar quejas y alegaciones (grievance)
  6. Una vez firmado el contrato, cualquier incidencia se resuelve mediante un mecanismo de quejas.

mWater es un entorno regulado y esta regulación se realiza a través de normas. Son de 3 tipos

  • definidas por el gobierno (p.ej. PHN)
  • definidas por las asociaciones de regantes
  • normas sociales

Otro aspecto importante es la gestión organizativa de los agetes que participan en cada escenario: cómo comparten normas, como se participa en actividades reguladas, emergencia y consolidación de normas comunes, toma de decisiones colaborativa y estudio de la dinámica de la propia organización.

Dado que la gestión de conflictos se realiza mediante quejas, es neceario definir las tecnologías necesarias: protocolos, artuitecturas, ténicas de negociación y argumentación... Los conflictos surgen del hecho de que algún participante no cumple su parte del contrato, por lo que habrá que poder validarlos y detectar estas situaciones. Todo esto se instrumenta a través de acuerdos.

Acaba explicando qué tareas del AT están relacionadas con qué partes del mWater. Un poco liado para contarlo aquí... pero podéis verlo en las transparenicas.

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Sistemas socio-ecológicos para la gestión de agua

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Es lo que voy a contar en ua charla de café, para explicar cómo Islander y Thomas encajan para modelar este tipo de sistemas (SES), aplicados al demostrador mWater, que es el que corresponde a nuestro grupo dentro del proyecto Agreeent Technologies.

Básicamente, he tratado de explicar brevemente las idesas de E. Ostrom, Premio Nobel de Economía del 2009, sobre el modelado de este tipo de sistemas y bajo qué condiciones las iniciativas de autoorganización pueden tener éxito. Juan Freire habla de lo mismo aplicado a urbanismo emergente.

Por si no hay copias  para todos o alguien de lo que no han asistido quiere descargar el documento, os lo dejo disponible aquí. Luego amplio este post para explicar alguna cosa más.


Y como siempre, cualquier comentario es bienvemido

Definición de agreement

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Dentro de un rato vamos a hablar sobre la definición de agreement (acuerdo). Esto no es lo que vamos a usar, pero me apetecía hacerlo y colgarlo para recordarlo bien. Son cosas que ya habíamos comentado en una reunión en junio de 2009 y que no había escrito en ningún lado, así que casi se me olvida. Menos mal que Antonio copió (y sabe dónde lo tenía, que es más importante) lo que dije entonces.

Así que, para que no me vuelva pasar, os dejo aquí las 5 dimensiones que forman el acuerdo: contexto, participantes, creación, contrato y ejecución. Con cada una de ellas he añadido algunas consideraciones que se me iban ocurriendo. Seguro que ni son todas las que están ni están todas las que son, pero puede ser un comienzo.

Y como slideshare no copia las transiciones, lo dejo en un vídeo. Es sólo un minutín, así que no te aburirás mucho. También puedes descargar la versión en  PDF. Y, como siempre, te recuerdo que cualquier comentario será bien recibido.

(he cambiado el vídeo de sitio porque se cortaba el audio)

Agreement Technologies and Social Neuroscience

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Los días 18 y 19 de  febrero tendrá lugar el workshop Agreement Technologies and Social Neuroscience, organizado dentro del proyecto Agreement Technologies. Se trata de un workshop multidiciplinar para tratar de comprender mejor cómo se pueden modelar acuerdos dentro de un contexto social entre

El año pasado asistí y la verdad es que resultó muy interesante: hablar con expertos de áreas que no tienen que ver nada con la mía... ni siquiera con la informática, descoloca un poco pero es muy enriquecedor. Si te gustan estas cosas te recomiendo que vayas. Si hay hueco, yo pretendo ir.

Si quieres saber algo más, aquí tienes el programa y los resúmenes de las ponencias.... y no voy a escribir más frases que empiecen con "si".

mWater: Geometría de las redes fluviales

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Detrás de este título tan atractivo y motivador se enconde un post... acorde con el título :-) Básicamente la idea es tener algo que nos sirva como ejemplo para empezar a estudiar los espacios de acuerdos y su dinamica en un problema más "Consolider". Y el ejemplo elegido es el modelado de una red fluvial. El propósito es poder usar los modelos de redes de consenso para tratar de modelar las interacciones en un sistema multiagente (SMA) para alcanzar un acuerdo. A través de los espacios de acuerdo puede llegarse a un conjunto de restricciones unidas a las leyes que regulan la propia dinámica del río. Y con algoritmos bien conocidos para resolver problemas de consenso en redes, a partir de su matriz Laplaciana

$latex \dot{x} = -Lx $,

donde $latex L = [l_{ij}] $ mantiene el grado de los nodos de la red y se define como

$latex l_{ij} = -1$ si $latex i \neq j$ y $latex l_{ij} = |N_i|$ si $latex i = j$

Para poder aplicarlo, es necesario disponer de un modelo de una red fluvial sobre el que se pueda aplicar este formalismo. El modelo de Scheidegger (1967) define la red como un grafo dirigido aleatorio sobre un retículo triangular. En cada intersección, se escoge al azar entre las dos posibles ramificaciones (izquierda o derecha). De esta manera, cada rama del rio es capaz de drenar una superficie de $latex \alpha^2$, donde $latex \alpha$ es la distancia entre dos vecinos (es decir, que las distancias entre cada segmento de río es de $latex \alpha$ -por simplicidad se suele escoger la unidad-).


Por otra parte, para determinar la relación entre el área de drenaje de un río y la longitud de su flujo principal se emplea la Ley de Hawk (1957):

$latex l \alpha a^h \centerdot $

donde l es la longitud del flujo principal, a es el área y el exponente de Hack h se calcula empíricamente y se encuentra en el rango 0.5-0.7. Con estos parámetros, es posible generar aleatoriamente redes que modelan un rio de forma realista, con lo que es posible generar distintos casos de prueba y comprobar empíricamente la validez de las propuestas.

Sobre estos modelos de rios, habrá que distribuir una red de agentes que representen las distintas entidades y personas responsables de la gestión y del consumo de los recursos hídricos. Creo que lo más adecuado es modelarlo como una red de tipo small world que simule las relaciones existentes entre los participantes. de esta forma es posible modelar las relaciones cercanas (entre regantes de una misma comarca) pero también la existencia de posibles relaciones lejanas que podrían modelar incluso a regantes de otras cuencas. Pero esta parte de la red social de riego la dejo para otra anotación

Para más información...

Dodds, Peter Sheridan: Geometry of river networks.- Thesis (Ph.D.)--Massachusetts Institute of Technology, Dept. of Mathematics, 1969. (PDF)


J. T. Hack. Studies of longitudinal stream profiles in Virginia and Maryland. In U.S.Geol. Surv.Prof. Pap., 294-B:45–97, 1957.

A. E. Scheidegger. A stochastic model for drainage patterns into an intramontane trench. In Bull. Int. Assoc. Sci. Hydrol., 12(1):15–20, 1967.

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