Michael Rovatsos

Reasoning About Communication – A Practical Approach Based on Empirical Semantics

Given a specification of communication rules in a multiagent system (in the form of protocols, ACL semantics, etc.), the question of how to design appropriate agents that can operate on such a specification is a very important one. In open systems, the problem is complicated even further by the fact that adherence to such a supposedly agreed specification cannot be ensured on the side of other agents.

Using trust for detecting deceitful agents in artificial societies

Trust is one of the most important concepts guiding decision-making and contracting in human societies. In artificial societies, this concept has been neglected until recently. The inherent benevolence assumption implemented in many multiagent systems can have hazardous consequences when dealing with deceit in open systems. The aim of this paper is to establish a mechanism that helps agents to cope with environments inhabited by both selfish and cooperative entities. This is achieved by enabling agents to evaluate trust in others. A formalization and an algorithm for trust are presented so that agents can autonomously deal with deception and identify trustworthy parties in open systems. The approach is twofold: agents can observe the behavior of others and thus collect information for establishing an initial trust model. In order to adapt quickly to a new or rapidly changing environment, one enables agents to also make use of observations from other agents. The practical relevance of these ideas is demonstrated by means of a direct mapping from a scenario to electronic commerce.

An integrated framework for adaptive reasoning about conversation patterns

We present an integrated approach for reasoning about and learning conversation patterns in multiagent communication. The approach is based on the assumption that information about the communication language and protocols available in a multiagent system is provided in the form of dialogue sequence patterns, possibly tagged with logical conditions and instance information. We describe an integrated social reasoning architecture m2InFFrA that is capable of (i) processing such patterns, (ii) making communication decisions in a boundedly rational way, and (iii) learning patterns and their strategic application from observation.

Towards Improving Supply Chain Coordination through Agent-Based Simulation

One of the most significant paradigm shifts of modern business management is that individual businesses no longer compete as autonomous entities but rather as supply chains. However, the majority of companies, especially small and medium enterprises, fail to design and manage their supply chains in a profitable way, as it is difficult to understand the complex dynamics of Supply Chain Management (SCM). In this paper we argue that agent technologies can provide an intelligent solution to the improvement of SCM. We present a multiagent-based framework for simulating supply chain (SC) operation and re-configuration, with the vision of helping to improve overall SC performance and coordination. The suggested key innovation lies in the better explanation of simulation results and its attractiveness to SCM practitioners. Its theoretical conceptualisation, a logic-based formalisation and the system’s architecture that combines agent technologies with business rules and business process modelling are presented.

Agents and Computational Autonomy

In this paper we contend that adaptation and learning are essential in designing and building autonomous software systems for reallife applications. In particular, we will argue that in dynamic, complex domains autonomy and adaptability go hand by hand, that is, that agents cannot make their own decisions if they are not provided with the ability to adapt to the changes occurring in the environment they are situated. In the second part, we maintain the need for taking up animal learning models and theories to overcome some serious problems in reinforcement

Fog Orchestration for Internet of Things Services

Large-scale Internet of Things (IoT) services such as healthcare, smart cities, and marine monitoring are pervasive in cyber-physical environments strongly supported by Internet technologies and fog computing. Complex IoT services are increasingly composed of sensors, devices, and compute resources within fog computing infrastructures. The orchestration of such applications can be leveraged to alleviate the difficulties of maintenance and enhance data security and system reliability. However, efficiently dealing with dynamic variations and transient operational behavior is a crucial challenge within the context of choreographing complex services. Furthermore, with the rapid increase of the scale of IoT deployments, the heterogeneity, dynamicity, and uncertainty within fog environments and increased computational complexity further aggravate this challenge. This article gives an overview of the core issues, challenges, and future research directions in fog-enabled orchestration for IoT services. Additionally, it presents early experiences of an orchestration scenario, demonstrating the feasibility and initial results of using a distributed genetic algorithm in this context.

A framework for agent-based distributed machine learning and data mining

This paper proposes a framework for agent-based distributed machine learning and data mining based on (i) the exchange of meta-level descriptions of individual learning processes among agents and (ii) online reasoning about learning success and learning progress by learning agents. We present an abstract architecture that enables agents to exchange models of their local learning processes and introduces a number of different methods for integrating these processes. This allows us to apply existing agent interaction mechanisms to distributed machine learning tasks, thus leveraging the powerful coordination methods available in agent-based computing, and enables agents to engage in meta-reasoning about their own learning decisions. We apply this architecture to a real-world distributed clustering application to illustrate how the conceptual framework can be used in practical systems in which different learners may be using different datasets, hypotheses and learning algorithms. We report on experimental results obtained using this system, review related work on the subject, and discuss potential future extensions to the framework.

Social Collective Intelligence: Combining the Powers of Humans and Machines to Build a Smarter Society

The book focuses on Social Collective Intelligence, a term used to denote a class of socio-technical systems that combine, in a coordinated way, the strengths of humans, machines and collectives in terms of competences, knowledge and problem solving capabilities with the communication, computing and storage capabilities of advanced ICT. Social Collective Intelligence opens a number of challenges for researchers in both computer science and social sciences; at the same time it provides an innovative approach to solve challenges in diverse application domains, ranging from health to education and organization of work. The book will provide a cohesive and holistic treatment of Social Collective Intelligence, including challenges emerging in various disciplines (computer science, sociology, ethics) and opportunities for innovating in various application areas. By going through the book the reader will gauge insight and knowledge into the challenges and opportunities provided by this new, exciting, field of investigation. Benefits for scientists will be in terms of accessing a comprehensive treatment of the open research challenges in a multidisciplinary perspective. Benefits for practitioners and applied researchers will be in terms of access to novel approaches to tackle relevant problems in their field. Benefits for policy-makers and public bodies representatives will be in terms of understanding how technological advances can support them in supporting the progress of society and economy.

An approach to the analysis and design of multiagent systems based on interaction frames

This paper introduces InFFrA, a novel method for the analysis and design of multiagent systems that is based on the notions of interaction frames and framing. We lay out a conceptual framework for viewing multiagent systems (MAS) as societies consisting of socially intelligent agents that record and organise their interaction experience so as to use it strategically in future interactions. We also provide criteria for the class of MAS InFFrA is suited for. The benefits of our approach are that it helps to understand and develop socially intelligent agents as well as to identify shortcomings of existing MAS. The method is evaluated through the analysis of an opponent classification heuristic that is used to optimise strategic behaviour in multiagent games, and interesting issues for future research are discussed.

Expectation-oriented modeling

This work introduces expectation-oriented modeling (EOM) as a conceptual and formal framework for the modeling and influencing of black- or gray-box agents and agent interaction from the viewpoint of modelers like artificial agents and application designers. EOM is unique in that autonomous agent behavior is not restricted in advance, but only if it turns out to be necessary at runtime, and does so exploiting a seamless combination of evolving probabilistic and normative behavioral expectations as the key modeling abstraction and as the primary level of analysis and influence. Expectations are attitudes which allow for the relation of observed actions and other events to the modelers intentions and beliefs in an integrated, adaptive manner. In this regard, this work introduces a formal framework for the representation and the semantics of expectations embedded in social contexts. We see the applicability of EOM especially in open domains with a priori unknown and possibly unreliable and insincere actors, where the modeler cannot rely on cooperation or pursue her goals through the exertion of strictly normative power, e.g. the development and assertion of flexible interaction policies for trading platforms in the Internet, as illustrated in a case study. To our knowledge, EOM is the first approach to the modeling, cognitive analysis and influencing of social interaction that aims at tackling the level of expectations explicitly and systematically.