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

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)

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“

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.

This symposium will be organized by the Biomedicine, Intelligent System and Educational Technology Reseach Group (BISITE) of the University of Salamanca. The technology transfer in this field is still a challenge and for that reason this type of contributions will be specially considered in this symposium. This conference is the forum in which to present application of innovative techniques to complex problems.

The artificial intelligence is changing our society. Its application in distributed environments, such as the Internet, electronic commerce, mobile communications, wireless devices, distributed computing, and so on is increasing and is becoming an element of high added value and economic potential, both industrial and research. These technologies are changing constantly as a result of the large research and technical effort being undertaken in both universities and businesses. The exchange of ideas between scientists and technicians from both academic and business areas is essential to facilitate the development of systems that meet the demands of today’s society.

DCAI 2010 is sponsored by the IEEE Systems Man and Cybernetics Society, Spain Section Chapter. The accepted papers included in DCAI 2010 proceedings (long papers, short papers and doctoral consortium papers) will be published by Springer Verlag in the Advances in Intelligent and Soft-Computing series of Springer. At least one of the authors will be required to register and attend the symposium to present the paper in order to include the paper in the conference proceedings.

(Read the complete Call for Papers)

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”.

Como el vídeo incluye pantallas de la aplicación, se ve mejor en pantalla completa o directamente la versión en HD en vimeo.

Sobre todo, gracias a Elena y a Natalia por el esfuerzo en tenerlo todo listo a tiempo.

Measuring the quality of answers in agent’s conversations. Quality measures:
precision (smaller interval) and certainly (how close is to true or false). They are related: precision is more interesting when certainly is close to true or false. Talking about absolute (values for the facts) and relative (external view) quality. But explaining all these thing he’s run out of time, so he can’t explain how agents can use this quality measures.

Designing Automated Agents Capable of Efficiently Negotiating with People – Overcoming the Challenge
Raz Lin

It’s very difficult to design domain independent agents that negotiate with other agents or with people, and this is the goal of the paper. HE establihes the negotiation environment, how the agent isdesigned and shows some samples in games andother environments: Diplomacy, autONA, Cliff-Edge… finishing with the KBAgent, which includes all the characteristics developed in the previous ones

• Generic agent / domain independent
• Qualitative decision making
• Non deterministic behavior / randomization
• Incorporating data from past interactions

And now something about validation. It is a problem because it is no standard fto do that. What is a ‘good’ agent? maximal payoff/maximal social welfare/end with agreement/pass Turing test? This is an open question.

A good question from Ingrid Nunes abuot emotions, because their influence in human negotiations. But at the moment they are not taken into account

Sorry, I’m a bit late, but this is the ideas that I’ve seen before, so I hope I’ll be able to complete this part later.

Measures of Context-Awareness for Self-Organizing Systems
Andrei Olaru

Points in common of selforg and ambient intelligence (a boring part about reactive and congnitive agents that I’m not going to comment) and now a bit about context-awareness. Agents connected in agrid with 8 neig (similiar to Kleinberg structure). Two measures: (i) pressure: how important a piece of information is; it represents urgency and determines how important the information is and how it is propagated; and (ii) interest (similar to attention focus) related with data, agents and facts. Showing how information is propagated.

Dynamic Evolution of Role Taxonomies through Multidimensional Clustering in Multiagent Organizations
Ramon Hermoso (a.k.a. Dani Mateo :-)

Related with adaptive organizations. When an agent arrives to a organization, it has to choose a counterpart to interact with. They propose a mechanism to create role taxonomies and to allow them to evolve (that is, to modificate them and, sometimes, to create new ones).

Some comments: Are always new roles specializations of existing ones? Are new roles based on existing actions? Removing roles? Has it any sense? (better remove it when there is no agents in it? who decides the role fora existing agents (supervision??

A Taxonomy of Adaptive Systems
Jan Calta

Talking about self-* properties:

• self-stabilization (SSS) = closure +convergence
• selg-organization (SOS) = adaptability +decentralization + local knowledge + homogeneity
• self-management (SMS)  = configuration + optimization + healing (fixing)+ protection
• self-adaptation (SAS) = can be considered as a generalization of SSS, SOS and SMS.

Conparing these three approaches in terms of adaptation, architecture and the approach used in their design. Interesting to describe correctly consensus networks. I think that is a SOS+SSS system (importance of the convergence)

Tags: eumas09, self-organization, organization, adaption,