The ANTE platform is being developed in LIACC/NIADR (Distributed AI and Intelligent Robotics Group), at the University of Porto, Faculty of Engineering.
This work is supported by FCT (the portuguese Foundation for Science and Technology) and the Agreement Technologies COST Action.


Negotiation and task allocation have been in the multi-agent systems realm since its inception as a research field. More recently, social aspects of agenthood have received increasing attention, namely developing on the fields of normative and trust systems.
The ANTE framework encompasses results of research efforts on three main agreement technology concepts, namely negotiation, normative environments and computational trust.
ANTE is therefore the corollary of an ongoing long-term research project, which has been targeting the domain of B2B electronic contracting, although being conceived as a more general framework having in mind a wider range of applications.

ANTE addresses the issue of multi-agent collective work in a comprehensive way, covering both negotiation as a mechanism for finding mutually acceptable agreements, and the enactment of such agreements. Furthermore, the framework also includes the evaluation of the enactment phase, with the aim of improving future negotiations.
Taking a broad perspective, an agreement can in this context be a solution obtained using a distributed cooperative problem solving approach. Therefore, a wide range of problems can be tackled. The agreement binds each negotiation participant to its contribution to the overall solution. It is therefore useful to represent the outcome of a successful negotiation process in a way that allows for checking if the
contributions of each participant do in fact contribute to a successful execution of the agreement. A normative environment, within which agent interactions that are needed to enact the agreement will take place, takes care of this monitoring stage.
Assessing the performance of each contribution is essential to enhance future negotiations. Computational trust may therefore be used to appropriately capture the trustworthiness of negotiation participants, both in terms of the quality of their proposals when building the solution (i.e. the practicability of the approach) and in terms of their ability to successfully enact their share.


Negotiation is a form of decision-making where two or more parties jointly search a space of possible solutions with the goal of reaching a consensus. People use negotiation as a means of compromise in order to reach mutual agreements. In general, negotiation is defined as an interactive process whose goal is to achieve an agreement between interested parties. In ANTE, we have developed a negotiation protocol (Q-Negotiation) suitable for both competitive and cooperative environments that conducts to the selection of the best possible solutions.
It encompasses two important features:

  • A multi-attribute evaluation to select the most favorable proposals at each round.
  • A learning capability in order to enable agents to make the best possible deals even when faced with incomplete information and when operating in dynamic environments.



Normative Environment

The normative dimension of a multi-agent system may, in general, encompass two perspectives on the interactions that norms are supposed to govern. Norms regulating pre-established interactions apply the agent population as a whole, e.g. by specifying appropriate interaction conventions for negotiation. On the other hand, run-time norms are those that come into force when agents negotiate or adopt them to govern subsequent interactions (e.g. negotiated contracts). Within ANTE we are mostly concerned with the latter case, i.e., with norms that are agreed upon through a negotiation process.
In the context of agreement technologies, the role of a normative environment is twofold. Given the agreement on a possible solution as obtained from the negotiation phase, it is necessary to check if the partial contributions of individual agents make their way in enabling a successful overall resolution of the problem.
In many cases, the execution of the solution is itself distributed, which requires agents to enact by themselves their part of the agreement. Monitoring this phase is therefore an important task. Furthermore, in non-cooperative or dynamic scenarios, it is possible that after successfully negotiating an agreement self-interested agents are no longer willing to fulfill their commitments. This puts in evidence the second role of a normative environment, that of enforcing norms by coercing agents to stand for their commitments.
The notion of norm has been used with different meanings. In ANTE, a norm is a rule prescribing some behavior that agents governed by that norm must meet in certain circumstances.




Although trust is typically associated with uncertainty (for instance, in business transactions), it is an ubiquitous social concept present in everyday life. In fact, trust has been studied in several research areas in distinct fields such as close relationships, political relationships between countries, social networks, and even cooperative relationships. Computer science scholars, especially in the area of multi-agent
systems, have been proposing computational models of trust that can be used to assist the decision making of agents when negotiating agreements, particularly in the phase of resource allocation.
Current research on computational trust models addresses the estimation of the trustworthiness of the target entities (individuals, groups, institutions, or things) by aggregating past evidence on these entities. These models tend to focus on some particular problem of computational trust, such as the modeling of the dynamics of trust, the context in which the evidence was produced, the use of reputation as a trust antecedent, and the modeling of trust in a socio-cognitive perspective.
Our approach to computational trust has as it main desideratum the ability to compute adequate estimations of trustworthiness in several different environments, including those of high dynamicity, where evidence on the agent in evaluation is scarce or even inexistent.

Ontology Services

An intrinsic problem that must be dealt with when approaching open systems is that each of a set of heterogeneous entities may potentially use a different domain ontology. This heterogeneity is a critical impediment to efficient business information exchange and to the automation of B2B processes.

ANTE includes ontology services in order to deal with this heterogeneity problem. The ontology-mapping service is aligned with a negotiation mediation service, allowing negotiation to take place between entities using different domain ontologies.