Modelling of Multi-Agent Systems: Experiences with Membrane Computing and Future Challenges

Formal modelling of Multi-Agent Systems (MAS) is a challenging task due to high complexity, interaction, parallelism and continuous change of roles and organisation between agents. In this paper we record our research experience on formal modelling of MAS. We review our research throughout the last decade, by describing the problems we have encountered and the decisions we have made towards resolving them and providing solutions. Much of this work involved membrane computing and classes of P Systems, such as Tissue and Population P Systems, targeted to the modelling of MAS whose dynamic structure is a prominent characteristic. More particularly, social insects (such as colonies of ants, bees, etc.), biology inspired swarms and systems with emergent behaviour are indicative examples for which we developed formal MAS models. Here, we aim to review our work and disseminate our findings to fellow researchers who might face similar challenges and, furthermore, to discuss important issues for advancing research on the application of membrane computing in MAS modelling.Comment: In Proceedings AMCA-POP 2010, arXiv:1008.314

SOLACE: A framework for electronic negotiations

Copyright @ 2011 Walter de Gruyter GmbHMost existing frameworks for electronic negotiations today are tied to specific negotiation systems for which they were developed, preventing them from being applied to other negotiation scenarios. Thus, the evaluation of electronic negotiation systems is difficult as each one is based on a different framework. Additionally, each developer has to design a new framework for any system to be developed, leading to a ‘reinvention of the wheel’. This paper presents SOLACE—a generic framework for multi-issue negotiations, which can be applied to a variety of negotiation scenarios. In contrast with other frameworks for electronic negotiations, SOLACE supports hybrid systems in which the negotiation participants can be humans, agents or a combination of the two. By recognizing the importance of strategies in negotiations and incorporating a time attribute in negotiation proposals, SOLACE enhances existing approaches and provides a foundation for the flexible electronic negotiation systems of the future

Object-Oriented Dynamics Learning through Multi-Level Abstraction

Object-based approaches for learning action-conditioned dynamics has demonstrated promise for generalization and interpretability. However, existing approaches suffer from structural limitations and optimization difficulties for common environments with multiple dynamic objects. In this paper, we present a novel self-supervised learning framework, called Multi-level Abstraction Object-oriented Predictor (MAOP), which employs a three-level learning architecture that enables efficient object-based dynamics learning from raw visual observations. We also design a spatial-temporal relational reasoning mechanism for MAOP to support instance-level dynamics learning and handle partial observability. Our results show that MAOP significantly outperforms previous methods in terms of sample efficiency and generalization over novel environments for learning environment models. We also demonstrate that learned dynamics models enable efficient planning in unseen environments, comparable to true environment models. In addition, MAOP learns semantically and visually interpretable disentangled representations.Comment: Accepted to the Thirthy-Fourth AAAI Conference On Artificial Intelligence (AAAI), 202