Software & Tools

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Framework/Infrastructure: Obligations specification and monitoring using Semantic Web Technology.

Author:

  • Nicoletta Fornara (University of Lugano, via G. Buffi 13, 6900 Lugano, Switzerland)
  • Marco Colombetti (Politecnico di Milano, piazza Leonardo Da Vinci 32, Milano, Italy)

E-Mail: {nicoletta.fornara, marco.colombetti}@usi.ch, marco.colombetti@polimi.it

Link: http://www.people.lu.unisi.ch/fornaran/ObligationsOntology.html

Description:

In our past works we have proposed the OCeAN meta-model for the specification of artificial institutions and for the definition of the commitment-based semantics of an Agent Communication Language (ACL) [2, 3]. This meta-model can be used for the design of open interaction systems where agents may interact, negotiate, and reach agreements on their future actions.

Recently we started to specify the notion of obligation, which is fundamental in our model, using Semantic Web Technology[4,1]. In particular we specified the ontology for representing obligations, the state of the interaction, and for monitoring the evolution of the state of the obligations using OWL 2 DL3 and SWRL (Semantic Web Rule Language)4 rules, we used OWL-API 3.2.35 for updating the ontology from a Java program, and HermiT 1.3.46 for reasoning on the ontology.

The crucial characteristics of the proposed ontology are:

  • It is possible to represent obligations whose content is related to time, with activation condition and deadline;
  • The content of the obligations is a class of possible actions, one of these actions has to be performed within a given deadline in order to fulfill the obligation;
  • The state of obligations is monitored using OWL axioms and a Java program that is necessary to be able to perform close world reasoning on certain classes. This because we assume that in the interaction contexts where this model will be used, not being able to infer that action has been performed in the past is sufficient evidence that the action has not been performed.

Framework/Infrastructure: CArtAgO

Authors: A. Ricci, A. Santi

E-Mail: a.ricci@unibo.it

Link: http://cartago.sourceforge.net

Description:

CArtAgO is a framework and infrastructure for developing and running general-purpose distributed computational environments for multi-agent systems, designed in terms of artifacts as first-class abstraction defined by the A&A conceptual/meta-model [3].

The background idea is to have environments as first-class abstractions in MAS development [1] and - in particular - of environment programming [2], so exploiting the notion of environment not as a merely source of percepts and actions (typical AI view), but as an abstraction layer that can be designed and programmed to encapsulate functionalities and services that agents can share and use at runtime.

Such functionalities span from simply enabling and governing the access and interaction with the real external environment (being it sofware or hardware), hiding low-level details, to supporting the communication, coordination, and cooperation among agents, as well as their organisations.

In the case of CArtAgO, a (distributed) environment can be designed in terms of set of workspaces including a dynamic set of artifacts, representing tools and resources - programmed by the MAS developer - that the agents can dynamically create, use, compose and dispose as first-class entity of their world.

CArtAgO is orthogonal with respect to the specific agent model adopted [5], so it can be integrated and used by agents possibly based on heterogeneous computation model & architecture, so as to develop open multi-agent systems possibly composed by heterogeneous agents sharing and interacting inside the same artifact-based environments. In spite of this orthogonality, the A&A model and CArtAgO it have been conceived to be particularly effective when integrated with cognitive agent languages/frameworks/platforms, such as the one based on the BDI model/architecture.

CArtAgO technology includes [4]:

  • a Java based API to program the environments
  • a runtime / infrastructure to run the environments, possibly distributed over the network
  • bridges that allow for integrating CArtAgO with different kinds of agent programming languages/framwork/platforms. The most updated one is c4jason bridge - which is the core of a family of platforms based on the tight integration of Jason and CArtAgO (JaCa, JaCaMo, JaCa-Android). Other bridges, still to be updated for the most recent version of CArtAgO, include bridges to Jadex and AgentFactory. Besides these ones, a raw Java API is provided to build custom bridges to what ever existing agent platform.

[1] D. Weyns, A. Omicini, J. J. Odell, Environment as a first-class abstrac- tion in multi-agent systems, Autonomous Agents and Multi-Agent Systems 14 (1) (2007) 5–30.

[2] A. Ricci, M. Piunti, and M. Viroli. Environment programming in multi-agent systems: an artifact-based perspective. Autonomous Agents and Multi-Agent Systems, 23:158–192, 2011

[3] A. Omicini, A. Ricci, M. Viroli, Artifacts in the A&A meta-model for multi- agent systems, Autonomous Agents and Multi-Agent Systems 17 (3) (2008) 432–456.

[4] A. Ricci, M. Piunti, M. Viroli, A. Omicini, Environment programming in CArtAgO, in: R. H. Bordini, M. Dastani, J. Dix, A. El Fallah-Seghrouchni (Eds.), Multi-Agent Programming: Languages, Platforms and Applica- tions, Vol. 2, Springer, 2009, pp. 259–288.

[5] A. Ricci, M. Piunti, L. D. Acay, R. Bordini, J. Hu ̈bner, and M. Dastani. Integrating artifact-based environments with heterogeneous agent-programming platforms. In Proceedings of 7th International Conference on Agents and Multi Agents Systems (AAMAS08), 2008.

Framework/Infrastructure: JaCaMo

Authors: O. Boissier, R. Bordini, J. Hubner, A. Ricci, A. Santi

E-Mail: a.ricci@unibo.it

Link: http://jacamo.sourceforge.net

Description: General-purpose platform for developing multi-agent systems based on Jason+CArtAgO+Moise providing an integrated approach to Multi-Agent Programming, including main programming dimensions such as Agent-Oriented Programming, Organisation-Oriented Programming and Environment Programming.

Framework/Infrastructure: GORMAS (Guidelines for ORganizational Multi-Agent Systems).

Authors:

  • Estefanía Argente
  • Sergio Esparcia
  • Vicente Julián
  • Vicente Botti

E-Mail: {eargente, sesparcia, vinglada, vbotti}@dsic.upv.es

Link: http://users.dsic.upv.es/grupos/ia/sma/tools/Gormas/index.php

Description:

GORMAS (Guidelines for ORganizational Multi-Agent Systems) defines a set of activities for the analysis and design of Virtual Organizations, including the design of their organizational structure and their dynamics. With this method, all services offered and required by the Virtual Organization are clearly defined, as well as its internal structure and the norms that govern its behavior.

GORMAS is based on a specific method for designing human organizations, which consists of diverse phases for analysis and design. These phases have been appropriately adapted to the MAS field, this way to catch all the requirements of the design of an organization from the agents’ perspective. Thus, the methodological guidelines proposed in GORMAS cover the typical requirement analysis, architectural and detailed designs of many relevant Organization-Centered Multi-Agent Systems (OCMAS) such as PASSI, SODA, AGR, INGENIAS… methodologies, but it also includes a deeper analysis of the system as an open organization that provides and offers services to its environment.

Framework/Infrastructure: Thomas MeTHods, Techniques and Tools for Open Multi-Agent Systems.

Authors:

  • Vicente Julián
  • Carlos Carrascosa
  • Miguel Rebollo

E-Mail: {vinglada, carrasco, mrebollo}@dsic.upv.es

Link:http://users.dsic.upv.es/grupos/ia/sma/tools/Thomas/overview.php

Description:

Technological evolution over recent years (Internet, www, electronic commerce, wireless connection etc.) has led to a new paradigm of “computing as interaction”. Under this paradigm, computing is something that is carried out through the communication between computational entities. In this sense, computing is an inherently social activity rather than solitary, leading to new forms of conceiving, designing, developing and managing computational systems. One example of the influence of this viewpoint is the emerging model of software as a service, for example in service-oriented architectures. The technology of agents/multiagent systems is particularly promising as a support for this new computing as interaction paradigm. Dynamic agent organisations that self-adjust in order to make the most of their current environment are increasingly important. These organisations could appear in dynamic or emerging societies of agents such as Grid domains, peer-to-peer networks, or other environments in which the agents coordinate in a dynamic way in order to offer composite services. The social factors in the organisation of multi-agent systems are also increasingly important for structuring interactions in dynamic open worlds. Within this interesting area, part of the more general field of Intelligent Systems, we can identify a number of important lines of research:

  • Dynamics/Regulation: flexibility in order to permit the entry and exit of agents, evolution of the organisational structure, regulation mechanisms etc.
  • Heterogeneity: different types of agents with different capabilities, runtime coordination (requiring semantic description of the capacities/services), different devices (physical device resources), different channels of communication (wireless, wireline, etc.)

The aim of the present project is to advance and contribute solutions in these areas, principally in the aspects related to organisational structures. In this sense, the following is proposed:

  • to develop a multiagent system architecture that is suitable for the generation of virtual organisations in open environments, as well as a support platform that will allow these systems to be implemented.
  • to design a method for developing open multiagent systems which is orientated towards the concept of organisation, and which will cover the complete life cycle of an open system, allowing the suitable management of large scale complex systems and giving specific support in order to meet the possible needs of these types of systems.
  • to develop a model of agents capable of taking decisions autonomously, equipped with learning mechanisms and able to respond to events, planning and replanning within runtime.
  • to develop intelligent service coordination techniques/methods within open, decentralised multiagent systems: intelligent service location (directory services, syntactic and semantic comparison techniques for services) and generation and adaptation of composed services.
  • to develop mechanisms based on organisational structures and virtual organisations that optimise and regulate the coordination of services in open multiagent systems.
  • to define the mechanisms that allow the interaction between agents in open distributed wireless systems in which problems of communication and security may occur.
  • to develop and implement the necessary security and privacy policies.
  • to develop various prototypes in order to validate the proposed architecture in collaboration with companies interested in the proposal and in the results that are obtained from the project.

You can find more information about the project at http://www.thomas-tin.org/

Framework/Infrastructure: MAGENTIX2: Open multi-agent systems platform.

Authors:

  • Ana García Fornes
  • Agustín Espinosa Minguet
  • Soledad Valero Cubas

E-Mail: {magentix2}@dsic.upv.es

Link: http://www.gti-ia.upv.es/sma/tools/magentix2

Description:

Magentix2 is an agent platform for open Multiagent Systems. Its main objective is to bring agent technology to real domains: business, industry, logistics, e-commerce, health-care, etc.

Technological evolution in the areas of Computer Technology and Communications has raised new challenges and requirements for software systems. Computation is now often conceived as an inherently social activity. Multiagent systems technology has some characteristics that indicate its potential to support this new paradigm of computation. Dynamic organizations of agents, which are able to automatically adjust their own behavior in order to make the most of their environment at any given time, are becoming more and more important. Social factors in the organizations of multi-agent systems are also becoming more important in order to structure the interactions in dynamic and open worlds.

Magentix2 project is proposed as a continuation of the Magentix project. The final goal is to extend the functionalities of Magentix, providing new services and tools that allow for the secure and optimized management of open multiagent systems.

Thus, the Magentix2 main objective is to develop technologies to cope with the (high) dynamism of the system topology and with flexible interactions, which are both natural consequences of the distributed and autonomous nature of the components. In this sense, the platform has been extended in order to support flexible interaction protocols and conversations, and interactions among agent organizations. Moreover, other important aspects cover by the Magentix2 project are the security issues.

Thus a security model that incorporates low-level security mechanisms and trust measures that complement the classical cryptographic methods is been developing. Among other functionalities, Magentix 2 incorporates a tracing service. This service allows agents to publish or to subscribe to some events which match with some determined attributes, receiving messages from the platform when the event occurs (when it is subscribed to it). Also, Magentix2 includes an argumentation API that allows agents to engage in argumentation dialogues to reach agreements about the best solution for a problem that must be solved. Finally, Magentix2 incorporates the THOMAS framework, allowing users to manage with organizational and service aspects easily.

This project has been funded by Ministerio de Ciencia e Innovación of the Spanish Government.

Framework/Infrastructure: Electronic Institutions Development Environment (EIDE).

Author:

  • Arcos Rosell, Josep Lluis
  • Bou Mocholí, Eva
  • García Camino, Andrés
  • Giovannucci, Andrea
  • Hernández, Carlos
  • Noriega B.V., Pablo
  • Rodríguez-Aguilar, Juan Antonio
  • Schorlemmer, Marco
  • Sierra Garcia, Carles

E-Mail: sierra@iiia.csic.es

Link: http://e-institutions.iiia.csic.es/

Description:

Electronic Institutions are a way to implement interaction conventions for agents - human or software - who can establish commitments on an open environment.

Our proposal involves:

• Exploration of flexible convention making and enforcement.
• Study the use of institutional constructs in social systems modelling.
• Development of an ”institutional” software layer to specify, activate and test electronic institutions.
• Experiments on actual E-Institutions.

Electronic Institutions Development Environment (EIDE) is composed of:

  • Islander: Software for the graphical specification of an electronic institution.
  • AMELI: Agent-based middleware for electronic institutions. The Electronic Institution execution platform.

Framework/Infrastructure: WPDSolver: Winner Determination Problem Solver.

Author: Vasirani, Matteo

E-Mail: matteo.vasirani@urjc.es

Link: http://www.agreement-technologies.org/software/bundle/wpdsolver/

Description:

In a combinatorial auction, a bid comprises a collection of items and the amount of money that the bidder is willing to pay for such items. Two bids are in conflict with each other if they share at least one item. In this case, the auctioneer cannot accept both bids in the set of the winner bids. A bid is characterised by its value and by its score, that is, the value divided by number of items. The problem that the auctioneer faces is selecting the set of non-conflicting bids that maximises the total value or the total score of the accepted bids. The bundle org.agreement_technologies.service.wdpsolver is an implementation based on stochastic local search for the resolution of the winner determination problem.

Framework/Infrastructure: Preferred Trust.

Author:

  • Sierra Garcia, Carles
  • Debenham, John

E-Mail: sierra@iiia.csic.es

Link: http://www.agreement-technologies.org/software/bundle/preferred_trust/

Description:

A trust service. Remind that we want to measure the trust in a supplier as the certainty we have in the fact that the supplier will do what he commits to. 1 The trust model used is a probabilistic approach. We represent the expected observation of a commitment as a probability distribution assessing a probability value to each possible observation. So, the certainty can be evaluated by the minimum relative entropy between the current probability distribution and a default one with maximum entropy.

An experience is a tuple of three elements: the commitment, the observation, and the time when the experience take place. Commitments are announcements of what should be done in the short future. After a commitment is made, the agent committed to can observe the execution of the commitment. Comparing each commitment with the corresponding observation, we can analyze the performance of an experience. Good experiences with a supplier will increase our trust in signing a new contract with it. The next steps explain what you have to do to could use the enactment trust service.

  1. Define the class used to represent the commands or orders ( will named as <T> ). It will be used to represent the demands (commitments) and what you get (observations).
  2. Create a class that implements a similarity function between two instances of T(org.agreement_ technologies.common.enactment_trust.SimilarityFunction ).
  3. Create a class that implements a enactment function of T( org.agreement_technologies.common.enactment_trust.EnactmentFunction ). org.agreement_technologies.common.enactment_trust.PreferredEnactmentFunction, in this case you must specify the PreferFunction, which measures the extent to which an observation is preferable to a commitment. org.agreement_technologies.common.enactment_trust.IdealEnactmentFunction, you must define the Ideal probability distribution in the constructor of IdealEnactmentFunction .
  4. Get the org.agreement_technologies.common.enactment_trust.EnactmentTrustEvaluatorFactory service. ServiceReference<EnactmentTrustEvaluatorFactory> reference =context .getServiceReference(EnactmentTrustEvaluatorFactory.class); EnactmentTrustEvaluatorFactory factory = context.getService(reference);
  5. Create a new trust evaluator using the similarity and enactment functions ( org.agreement_technologies.common.enactment_trust.EnactmentTrustEvaluatorFactory#createEvaluator(SimilarityFunction, EnactmentFunction) ) . This will be created using default settings, if you have enough knowledge of the algorithm, you can change them later.
  6. Now you could start to add experiences ( org.agreement_technologies.common.enactment_trust.EnactmentTrustEvaluator#addExperience(String, Object, Object, long)) . If you want to remove an experience you have to use the experiences iterator , or if you want to remove all the experiences of a supplier or all the suppliers you have to use the clear methods.
  7. Finally you could obtain the trust for an order an a supplier ( org.agreement_technologies.common.enactment_trust.EnactmentTrustEvaluator#trust(String, Object)} ) .

Framework/Infrastructure: Strategic E-Sourcing services (SES)

Author:

  • Sierra Garcia, Carles (IIIA-CSIC, Campus de la UAB, E-08193 Bellaterra, Catalonia (Spain)).
  • Debenham, John (IIIA-CSIC, Campus de la UAB, E-08193 Bellaterra, Catalonia (Spain)).

E-Mail: sierra@iiia.csic.es

Link: http://www.agreement-technologies.org/software/bundle/ses/

Description:
The Strategic E-Sourcing services: The objective of this sofwtare is to stablish a technologic framework capable of giving support to the buying process. It evaluates the supply activity of a company in a process that is called strategic e-sourcing, and that is a fundamental element of any supply chain scenario.
For example; Imagine you are the person in charge of buying the office supplies for your company. You have ordered thirty pens of a certain quality, and you asked to receive them by tomorrow. You reach an agreement with the supplier ‘The Happier’. Unfortunately, you receive sixty pencils two days late. You feel disappointed as you wanted pens, not pencils, and you needed them immediately, not two days latter. The supplier ‘The Happier’ is not trustworthy.
Your level of satisfaction with the outcome of the agreement will depend on: (1) how important was for your company each order dimension –in the example: product, quality, quantity and delivery day–, (2) how different is what you got –the observation– compared with what you asked for –the commitment– and, of course, (3) which are your preferences.
This bundles are an specialization of the preferred trust services focused in the stretegic e-sourcing. The main service provided by the bundles is the org.agreement_technologies.common.ses.SESAdmin. It follows the factory pattern, used to create org.agreement_technologies.common.ses.SESAnalyzer, that you can use to:

  • Manage the experience history.
  • Calculate the similarity of an order with the experience of the suppliers.
  • Calculate the satisfaction of an order with the experience of the suppliers.
  • Return the ranking of the best suppliers to realize some contracts. To realize this is necessary that the GLPK is installed in your system.
  • Calculate the trust that a commitment will be done.


Framework/Infrastructure: MAP Parser / Map Grounding.

Author:

  • Sapena, Oscar
  • Onaindía, Eva

E-Mail: {osapena, onaindia}@dsic.upv.es

Link: http://www.agreement-technologies.org/software/bundle/map_parser/

http://www.agreement-technologies.org/software/bundle/map_grounding/

Description:

Map Parser: Parser from planing tasks to Java objects.
The “map_parser” bundle provides a service for translating a planning task, described in a variant of the PDDL 3.1 language, to a set of Java objects. Concretely, the parser supports the following features of PDDL 3.1: definition of types, constants, objects, predicates, non-numeric functions and non-durative operators, possibly with negative preconditions.
The language has also a number of special features to allow the description of planning tasks distributed among several cooperative agents:

  • Definition of agents and multi-functions.
  • Definition of the information a particular agent can share with other agents. The remaining information is considered private.
  • Definition of belief rules.
  • Definition of the initial state in which each fact can be represented as true, false or unknown.
  • Declaration of global goals, that all agents must achieve, and private goals, that represent preferences of a particular agent.

The Parser interface acquires this information from two different files (domain and problem) and returns an object of the Task type, which allows to easily consult the parsed data.

Map Grounding: Grounding process over planning tasks.

The “map_grouding” bundle implements a grounding process for a parsed planning task. The parsed planning task (object of type Task) can be obtained by means of the “map_parser” service. In a grounding process, the open variables in the planning operators and facts are replaced by their possible values, obtaining a grounded planning task as a result, i.e. an object of type GroundedTask. The work with a grounded task is usually much easier for the planners, so most of the existing planners require a grounding stage like this.
The grounding service also has other useful functionalities:

  • The identification of static data, which is information that the agents cannot change through their actions. Removing this information from the planning task can significantly speed up the planning process.
  • A relaxed planning graph (RPG) construction. The RPG performs a reachability analysis, grounding only those actions that are feasible in a relaxed planning task (in which the negative effects of the actions are ignored). The RPG is also very useful to compute heuristic information about the cost of achieving a given fact.
  • Incremental construction. The re-ground service allows add new information received from other agents. The grounding data and the RPG are then updated with this new information.

The obtained grounded task contains all the information required by an agent to start the planning process.

Framework/Infrastructure: Probabilistic Negotiation.

Author:

  • Bella Sanjuán, Antonio
  • Ferri Ramírez, César
  • Hernández Orallo, José
  • Ramírez Quintana, María José

E-Mail: abella@dsic.upv.es

Link: http://www.agreement-technologies.org/software/bundle/probabilistic_negotiation/

Description:

We proposed the Best Local Expected profit (BLEP) and theMaximum Global Optimisation (MGO) negotiation strategies (both based on probabilities) that can be applied to a selling environment, where one seller offers his products to the most desirable customers. The objective of the seller is to obtain as maximum profit as possible in a negotiation process with a limited number of offers, where the product and/or the customer can change from one offer to another. Each offer is calculated taking into account the probability of buying for each customer, product and price. This probability can be estimated in several ways, for example, by an expert, or as we do, using information of past negotiation processes to learn a data-mining model that estimates the probability of new examples. The most innovative aspect of our bundle is that we have introduced the probability of buying each product, for each customer and for each price, in the negotiation model development, i.e., our negotiation strategies use those buying probabilities to calculate the offers which customer, which product, which price). As far as we know, using probabilities in a negotiation strategy is an innovative aspect in the field of automatic negotiation. Moreover, having the buying probability for each product, customer and price is an advantage for our negotiation strategies, because the offers can be done following a global profit maximisation criterion.

Framework/Infrastructure: Case-Based Argumentation Infrastructure For Agent Societies

Author:

  • Jaume Jordán
  • Stella Heras
  • Vicente Julián

E-Mail: {fjjordan,sheras,vingladag}@dsic.upv.es

Link: http://users.dsic.upv.es/~vinglada/docs/

Description:

Despite the important advances in the argumentation theory, it is difficult to find infrastructures of argumentation offering support for agents that have a social context. We propose an infrastructure to create and run argumentative agents in an open Multi-Agent System. This infrastructure offers the necessary tools to develop agents with argumentation capabilities, including the communication skills and the argumentation protocol, and it offers support for agent societies.

Argumentation theory has produced important benefits on many AI research areas, from its first uses as an alternative to formal logic for reasoning with incomplete and uncertain information to its applications in Multi-Agent Systems (MAS). Currently, the study of argumentation in this area has gained a growing interest. The reason behind is that having argumentation skills increases the agents’ autonomy and provides them with a more intelligent behaviour. An autonomous agent should be able to act and reason as an individual entity on the basis of its mental state (beliefs, desires, intentions, goals, etc.). As member of a MAS, an agent interacts with other agents whose goals could come into conflict with those of the agent. In addition, agents can have a social context that imposes dependency relations between them and preference orders among a set of potential values to promote/demote. For instance, an agent representing the manager of a company could prefer to promote the value of wealth (to increase the economic benefits of the company) over the value of fairness (to preserve the salaries of his employees). Therefore, agents must have the ability of reaching agreements that harmonise their mental states and that solve their conflicts with other agents by taking into account their social context. Argumentation is a natural way of reaching agreements between several parties with opposing positions about a particular issue. The argumentation techniques, hence, can be used to facilitate the agents’ autonomous reasoning and to specify interaction protocols between them.

In this work, we propose an infrastructure to create and run argumentative agents in a MAS. This infrastructure offers the necessary tools to develop agents with argumentation capabilities, including the communication skills and the argumentation protocol, and it offers support for agent societies. The main advantage of having this infrastructure is that it is possible to create agents with argumentation capabilities to resolve a specified problem. This approach can obtain better results than other distributed approaches due to the argumentation process between agents and their reasoning skills. In the argumentation dialogue the agents try to reach an agreement about the best solution to apply for each proposed problem.

Currently, the infrastructure has been implemented and tested in a real customer support application. All knowledge resources developed use ontologies as representation language. Concretely, we assume the existence of a domain-dependent ontology that represents the concepts of the application domain and we have created an argumentation ontology, called ArgCBROnto that agents that provides a common understanding about argumentation concepts for agents of agent societies. In addition, we have created OWL-API ontology parsers to manage the knowledge resources of our argumentation framework.

The main components of our infrastructure are the argumentative agents, the Commitment Store and the knowledge interchange mechanism. We consider different organizations or groups composed by some argumentative agents. The Commitment Store interacts with all the argumentative agents to store the positions and the arguments generated in the argumentation dialogue. The knowledge interchange is made with concepts of the ArgCBROnto ontology. The agent platform used in the implemented infrastructure is Magentix2. This is an agent platform that provides new services and tools that allow for the secure and optimised management of open MAS. The argumentative agents and the Commitment Store agent are extensions of the Magentix2 BaseAgent. Both the ontology and the parsers are now available, while the software to run the argumentation framework is being adapted to be able to use in generic problem solving applications and will be publicly available in a near future.

Framework/Infrastructure: Agreement Technologies Environment (ATE)

Author:

  • Carlos Carrascosa (UPV, SPAIN)
  • Marc Esteva (IIIA-CSIC, SPAIN)
  • Vicente Julián (UPV, SPAIN)
  • Marc Pujol (IIIA-CSIC, SPAIN)
  • Mario Rodrigo (UPV, SPAIN)
  • Juan A. Rodríguez-Aguilar (IIIA-CSIC, SPAIN)
  • Bruno Rosell i Gui (UDT-IA, IIIA-CSIC, SPAIN)
  • Matteo Vasirani (URJC, SPAIN)

E-Mail: rosell@iiia.csic.es

Link: http://www.iiia.csic.es/~rosell/ATE/

Description:

The Agreement Technologies Environment (ATE) must provide an environment where agents and/or humans can interact with each other to reach agreements.

This environment is aimed at supporting the establishment of coalitions, collaborations, negotiations and agreements on everything.

On the other hand, humans and / or agents participating in the ATE are not homogeneous, but each has its own objectives, their way of interacting, and can even be located in different locations. Namely the ATE should provide a heterogeneous environment where that humans as well as software agents can join.

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