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Existing approaches for the verification of normative systems consider limited representations of norms, often neglecting collective imperatives, deadlines and contrary-to-duty obligations. In order to capture the requirements of real-world scenarios, these structures are important. In this paper we propose methods for the specification and formal verification of complex normative systems that include contrary-to-duty, collective and event-driven imperatives with deadlines. We propose an operational syntax and semantics for the specification of such systems. Using Maude and its linear temporal logic model checker, we show how important properties can be verified for such systems, and provide some experimental results for both bounded and unbounded verification.

A multi-agent system can be analyzed and specified as an organization consisting of roles and their relations. The performance of an organization depends on many factors among which the type of its organizational structure, i.e., the set of relations holding between its roles. This work focuses on the structure of organizations and addresses the issue of the analysis, evaluation, and comparison of organizational structures which can contribute to develop general methods for the assessment of multi-agent systems’ performance. Specifically, quantitative concepts from graph theory are used to provide numerical analyses of organizational structures. It is argued that these analyzes can be used for evaluating to what extent an organizational structure exhibits some characteristic properties such as robustness, flexibility and efficiency.

Activities related to Knowledge Management (KM) processes require changes within the organizational processes in order to accommodate new goals and tasks, besides changing the way people view and do their work. In this paper, we argue that agents are suitable for modeling KM contexts, due to their cognitive characteristics, such as goals, beliefs, and reactivity. However, besides a good abstraction, the development of adequate solutions requires a consistent software engineering methodology. To help fill in this gap, we describe some results of our work on an integrated agent-oriented methodology to develop KM system, using a KM typical scenario. We consider all humans, organizations and existing systems as agents. The model enables the designer to understand and describe relations between entities before development of the system. Furthermore, different cognitive characteristics of agents are used in different phases of the development cycle.

Electronic commerce and trading of information goods significantly impact the role of intermediaries: consumers can bypass intermediating agents by forming direct links to producers. One reason that traditional intermediaries can still make a profit, is that they have more knowledge of the market, so trading via an intermediary saves on search costs [1], allowing the intermediary to charge a markup and make a profit. Another reason is trust and loyalty between consumers and intermediaries (cf. [6]). In this paper we investigate whether trust-based intermediating agents will also be able to make a profit in electronic markets, where other advantages of intermediaries (i.e., advantages of location or scale) disappear, but where trust and reputation mechanisms are becoming popular for helping consumers make purchasing decisions in the face of information overload on the world wide web [2]. We model an electronic market where agents trade an information good over a network. Buyer-agents use a decentralized reputation-based trust mechanism [4] to determine which connections to maintain to sellers of the good (producers and intermediaries), communicating with each other to establish trustworthiness of sellers to which they are not currently connected. The existence of such a reputation mechanism allows intermediating agents to accumulate a base of loyal consumer-agents who are willing to pay the intermediaries' markup in exchange for the intermediating agents' protection against the dynamics of the market place. Using computer simulations, we show that if market dynamics are sufficiently complex, trusted intermediaries are able to increase their market share and make a profit.

In this paper we discuss how current ontology concepts can be beneficial for more flexible and semantic rich description of the authoring process and for the provision of authoring support of Intelligent Educational Systems (IES) with respect to the three main authoring modules: domain editing, course composition and resource management. We take a semantic perspective on the knowledge representation within such systems and explore the interoperability between the various ontological structures for domain, instructional and resource modeling and the modeling of the entire authoring process. We build upon our research on Authoring Task Ontology and exemplify it within OntoAIMS system. We present authoring scenarios and show their mapping with authoring task ontology. Further we discuss the OntoAIMS framework for management of electronic learning objects (resources) and their usage in the automatic generation of course templates for the authors. Finally, we describe our architecture, based on the ontological specification of the authoring process.

The rapid dynamics of COVID-19 calls for quick and effective tracking of virus transmission chains and early detection of outbreaks, especially in the phase 2 of the pandemic, when lockdown and other restriction measures are progressively withdrawn, in order to avoid or minimize contagion resurgence. For this purpose, contact-tracing apps are being proposed for large scale adoption by many countries. A centralized approach, where data sensed by the app are all sent to a nation-wide server, raises concerns about citizens' privacy and needlessly strong digital surveillance, thus alerting us to the need to minimize personal data collection and avoiding location tracking. We advocate the conceptual advantage of a decentralized approach, where both contact and location data are collected exclusively in individual citizens' "personal data stores", to be shared separately and selectively, voluntarily, only when the citizen has tested positive for COVID-19, and with a privacy preserving level of granularity. This approach better protects the personal sphere of citizens and affords multiple benefits: it allows for detailed information gathering for infected people in a privacy-preserving fashion; and, in turn this enables both contact tracing, and, the early detection of outbreak hotspots on more finely-granulated geographic scale. Our recommendation is two-fold. First to extend existing decentralized architectures with a light touch, in order to manage the collection of location data locally on the device, and allow the user to share spatio-temporal aggregates - if and when they want, for specific aims - with health authorities, for instance. Second, we favour a longer-term pursuit of realizing a Personal Data Store vision, giving users the opportunity to contribute to collective good in the measure they want, enhancing self-awareness, and cultivating collective efforts for rebuilding society., Revised text. Additional authors

We introduce the formalism of infinite-state reactive modules to reason about the strategic behaviour of autonomous agents in a setting where data are explicitly exhibited in the systems description and in the specification language. Technically, we endow reactive modules with an infinite domain of interpretation for individual variables, and introduce FO-ATL, a first-order version of alternating time temporal logic, for the specification of properties of interest. We show that their verification is decidable for classes of data types of interest. This result is proved by defining a first-order version of alternating bisimulations and finite bisimilar abstractions. We illustrate the formal machinery by applying it to English and sealed bid auctions. In particular, we show that strategic properties of agents in auctions, including manipulability and collusion, can be expressed and verified in this framework.

We investigate the parameterised model checking problem for specifications expressed in alternating-time temporal logic. We introduce parameterised concurrent game structures representing infinitely many games with different number of agents. We introduce a parametric variant of ATL to express properties of the system irrespectively of the number of agents present in the system. While the parameterised model checking problem is undecidable, we define a special class of systems on which we develop a sound and complete counter abstraction technique. We illustrate the methodology here devised on the prioritised version of the train-gate-controller.

Coordination is essential to the effective operation of multi-agent systems. Convention emergence offers a low-cost and decentralised method of ensuring compatible actions and behaviour, without requiring the imposition of global rules. This is of particular importance in environments with no centralised control or where agents belong to different, possibly conflicting, parties. The timely emergence of robust conventions can be facilitated and manipulated via the use of fixed strategy agents, who attempt to influence others into adopting a particular strategy. Although fixed strategy agents have previously been investigated, they have not been considered in dynamic networks. In this paper, we explore the emergence of conventions within a dynamic network, and examine the effectiveness of fixed strategy agents in this context. Using established placement heuristics we show how such agents can encourage convention emergence, and we examine the impact of the dynamic nature of the network. We introduce a new heuristic, Life-Degree, to enable this investigation. Finally, we consider the ability of fixed strategy agents to manipulate already established conventions, and investigate the effectiveness of placement heuristics in this domain.\ud \ud

In this paper we present a rich decision model for intelligent agents involved in multi issue negotiations. The model, grounded on information theory, takes into account the aspects of trust and preference to devise mechanisms to manage dialogues. The model supports the design of agents that aim to take informed decisions taking into account that which they have actually observed.

This paper describes how cognitive modeling can be exploited in the design of software agents that support naval training sessions. The architecture, specifications, and embedding of the cognitive agent in a simulation environment are described. Subsequently, the agent's functioning was evaluated in complex, real life training situations for naval officers.