Miguel Rebollo

Después de acabar la asignatura del primer semestre siguiendo la filosofía de los nuevos grados del EEES, he tratado de recoger en un documento mi experiencia y mi valoración. En resumen, en mi caso el resultado ha sido bueno: ha aumentado el número de alumnos presentados, ha aumentado la tasa de alumnos aprobados y también han mejorado las calificaciones obtenidas.

Para mi, la obligatoriedad de la asistencia ha sido fundamental, ya que permite, como mencionaba Miguel Valero en un taller sobre adaptación al EEES, colocar a los alumnos en una situación de la que no pueden escapar y no les queda más remedio que aprender. Pero la asistencia no es algo mágico en sí misma si no va acompañada de un cambio en los métodos que consiga activar a los alumnos y que no vengan a “calentar la silla”.

Y también es importante el poder diseñar unos mecanismos de evaluación acordes con los métodos empleados, de forma que se pueda prescindir del examen final. La evaluación continua bien ejecutada es también una herramienta que permite evaluar procesos y no resultados, consiguiendo más información sobre el rendimiento de los alumnos que ayuda a tomar mejores decisiones sobre la valoración de su trabajo.

A continuación os dejo el documento completo para descargar, por si a alguien le sirve.

Uno grupo de niños de entre 8 y 10 años han publicado un artículo científico sobre el estudio que han realizado acerca del comportamiento de las abejas. En concreto, si son capaces de reconocer patrones y colores.

El artículo, Blackawton bees, se ha publicado en Biology Letters, una revista con un factor de impacto de 3,5. Mantiene el estilo original de los niños e incluso sus ilustraciones hechas a mano y pintadas con lápices de colores.

El artículo es el resultado de un proyecto de ciencia en la calle (Street Science) llevado acabo por el colegio de primaria Blackawton, en Devon (UK). Puedes consultar la página del proyecto.

Increíble lo que se puede hacer con unos buenos maestros y la motivación adecuada. ¿La enseñanza hoy en día es peor? Cosas como esta dicen que no ¿no te parece?

(fuente: Wired)

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

by Munindar Signh

Trust underlies all interactions among autonomous parties over many social relationships: casual, familiar, communal, organizational, practical…. But trust it use to be a internal characteristics and it can not be extrapolated outside a single application.

A social applications specifies and configure (i) roles,, (Ii) social interactions and (iii) additional constraints. And the elements we have available to model these systems (architecture) are components, connectors, constraints (over both of them) and patterns (that generalize its behavior). In the case of a social systems, they are
components >> individuals
connector >> social relationships
constraint >> reciprocal (ej Facebook)
patterns >> ….

The claim of this presentations is that trust is what flues in the relationships and it is how individuals and social relationships can be characterized. The sample> an agent (Toto) that acts as a middleware and can provide with trust the interactions among real people in different applications. That is, a layer which can be used in social apps (for instance) to measure the confidence on other users (humans) interactions (NOTE. a very close concept to the trust bundle proposed in AT)

by Sindhu Joseph

Abstract :
In this talk coherence-based models are proposed as an alternative to argumentation models for the reasoning of normative agents and normative deliberation. The model is based on Thagard’s theory of cognitive coherence and exploits the coherence relations that exist between claims and conclusion of arguments. A coherence-based model is intended to introduce more flexibility in the process of deliberation and agreement generation among normative agents. The basic coherence philosophy and what makes it interesting in the context of normative agents that deliberate to regulate a domain of interest are discussed.

This paper shows the application of coherence models to an argumentation model in a normative, regulated environment. I’m interested not in tris particular application, but in the coherence theory (Thagard).

Coherence estudies associations between pieces of information. It tríes to separate information in sets that mutually support the data. In some way, it can be consideres as a constraint satisfacción problem.

Different types of coherence can be identified: deductive, explanatory, deliberative, analoogous or conceptual, depending on the type of information. The Thagard model is a model of deductive coherence. It can be considered as a constraint satisfacción problem. But the main difference is that it does not try to maximize the partition (not the optimal -it is not needed to find a solution-)

Coherence applied to argumentation sees positive relates info as supporting arguments and negative weights as attacks to a claim.

Problem (general) How the coherence weights are calculated? Well, it is addressed in the questions: depends (roughly) on the number of arguments supporting a hypothesis.

Something interesting in the conclusiones: it can model different tupes of agente (utility maximizares, norm abiders, altruistic…) What about diferentt personalities? And a possibility for us: introduction of contexto as part of the future work.

More infomration, read Sindhu Jospeh PhD. thesis, “Copherence-Based Computational Agency“

Es el título de nuestro paper en las Jornadas que organiza el (Instituto de Matemática Multidisciplinar) de la UPV: Mathematical Models for Addictive Behaviour, Medicine & Engineering. El tema es el uso de redes de consenso para alcanzar acuerdos de forma descentralizada, aplicado en concreto a problemas de gestión de recursos hídricos. A continuación te dejo el resumen (en inglés) y las trasparencias de la presentación. En cuanto esté publicado dejaré también la referencia completa al artículo y, si puedo por temas de licencia, el enlace.

Abstract

A consensus network is a dynamic system that evolves in time. Consensus of complete network is reached if and only if . Has  been de demonstrated that a convergent and distributed consensus algorithm in discrete-time can be written as follows:

where denotes the set formed by all nodes connected to the node i (neighbors of i). The collective dynamics of the network for this algorithm can be written as , where  is the Perron matrix of a graph with parameter . The algorithm converges to the average (or other functions) of the initial values of the state of each agent and allows computing the average for very large networks via local communication with their neighbors on a graph.

The convergence of this method depends on the topology of the network and its convergence is usually exponential. But sometimes it not needed to reach a final agreement on a concrete value. This proposal uses consensus networks to determine if an agreement is possible among a set of entities. Agents can leave the agreement if its parameters are out of the expected bounds, so the consensus network can be used to detect the candidate agents to be members of the final agreement. All this process is solved in a self-organized way and each individual agent decides to belong or not to the final solution.

To show the validity of the present approach, a water market is presented as case of study. The water market is a case of complex social-ecological system (SES), where centralized and hierarchical approaches trend to fail and self-organized solutions seems to be more sustainable in the long term (Ostrom, 2009). In general, agreements related to natural resource management involve very complex negotiations among agents. Water demands and regulation is a very complex distributed domain appropriated for MAS.

An important question is if this kind of markets requires some regulation or not. From an exclusively economic point of view the dominant strategy for agents in deregulated markets is not cooperative because each agent wants to maximize exclusively his payoff, and therefore they are not interested in the global and socially efficiency of the natural resources.

En el grupo hemos comprado varias licencias de MacOS X Snow Leopard, pero con las licencias sólo han venido 2 DVD. Es un problema porque para actualizar los equipos nos va a llevar algo así como un mes… y eso si somos rápidos pasándoselo al siguiente. Está también el asunto de irte fuera varios días con el portátil y no poder llevar un disco del sistema por si las moscas. Y para acabar lo poco que me fio de la perdurabilidad de los soportes ópticos (tengo discos de hace 3 ó 4 años que ya no puedo leer).

Así que aquí simplemente te voy a contar como te puedes hacer una copia de un disco original. No es complicado y seguro que hay infinidad de alternativas. Simplemente se trata de crear un nuevo DVD maestro. Así es como lo he hecho yo

1. Abre la Utilidad de Discos y seleciona Nueva imagen
2. Selecciona la unidad de DVD (no el disco); esto no sé si tiene sentido
3. Elige como formato de la imagen DVD/CD maestro y graba la imagen
4. Una vez generada no la montes, simplemente introduce un DVD de doble capa (de los caros) y graba la imagen que acabas de generar en él.

Y, voilà, ya tienes una copia de un disco arrancable. No he probado, pero estoy seguro de que puedes usar la imagen que has generado para actualizar desde una versión anterior de MacOS sin tener que quemar un DVD.

Actualización 9-mar.: Acabo de probar a actualizar directamente con la imagen del disco duro (aún no la había borrado) y NO SE PUEDE. Al ejecutar la aplicación de instalación aparece un mensaje indicando que para poder actualizar el S.O. hay que grabar la imagen en un DVD.

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)

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.