Moser Silva Fagundes

Using Normative Markov Decision Processes for evaluating electronic contracts

Before signing electronic contracts, a rational agent should estimate the expected utilities of these contracts and calculate the violation risks related to them. In order to perform such pre-signing procedures, this agent has to be capable of computing a policy taking into account the norms and sanctions in the contracts. In relation to this, the contribution of this work is threefold. First, we present the Normative Markov Decision Process, an extension of the Markov Decision Process for explicitly representing norms. In order to illustrate the usage of our framework, we model an example in a simulated aerospace aftermarket. Second, we specify an algorithm for identifying the states of the process which characterize the violation of norms. Finally, we show how to compute policies with our framework and how to calculate the risk of violating the norms in the contracts by adopting a particular policy.

Supporting Medical Emergencies by MAS

In the emerging field of m-Health, advanced applications provide healthcare to people anywhere, anytime using broadband and wireless communications, as well as mobile computing devices. The notion of Service-Oriented Multi-Agent Systems (SOMAS) that has recently been proposed appears to adequately capture the requirements of applications in this field. The THOMAS abstract architecture and software platform supports the construction of SOMAS around an organisational metaphor. In this paper we present an application prototype built on top of THOMAS for a real-world mHealth scenario: mobile medical emergency management in an urban area.

Analyzing the tradeoff between efficiency and cost of norm enforcement in stochastic environments

In multiagent systems, agents might interfere with each other as a side-effect of their activities. One approach to coordinating these agents is to restrict their activities by means of social norms whose violation results in sanctions to violating agents. We formalize a normative system within a stochastic environment and norm enforcement follows a stochastic model in which stricter enforcement entails higher cost. Within this type of system, we provide an approach to analize the tradeoff between norm enforcement efficiency and its cost considering a population of norm-aware selfish agents.

Imperfect Norm Enforcement in Stochastic Environments: An Analysis of Efficiency and Cost Tradeoffs

In heterogeneous multiagent systems, agents might interfere with each other either intentionally or unintentionally, as a side-effect of their activities. One approach to coordinating these agents is to restrict their activities by means of social norms whose compliance ensures certain system properties, or otherwise results in sanctions to violating agents. While most research on normative systems assumes a deterministic environment and norm enforcement mechanism, we formalize a normative system within an environment whereby agent actions have stochastic outcomes and norm enforcement follows a stochastic model in which stricter enforcement entails higher cost. Within this type of system, we analyze the tradeoff between norm enforcement efficiency (measured in number of norm violations) and its cost considering a population of norm-aware self-interested agents capable of building plans to maximize their expected utilities. Finally, we validate our analysis empirically through simulations in a representative scenario.

Normative reasoning with an adaptive self-interested agent model based on Markov decision processes

Rational self-interested agents, which act so as to achieve the best expected outcome, should violate the norms if the expected rewards obtained with the defections from the norms surpass the expected rewards obtained by being norm-compliant. It means they should estimate the earnings brought about by the violations and the losses caused by their respective reactions. In this paper, we present a rational self-interested agent model that takes into account the possibility of breaking norms. To develop such model, we employ Markov Decision Processes (MDPs). Our approach consists of representing the reactions for norm violations within the MDPs in such a way that the agent is able to reason about how those violations affect her expected utilities and future options. Finally, we perform an experiment in order to establish comparisons between the model presented in this work and its norm-compliant version.

An Organisation-Based Multiagent System for Medical Emergency Assistance

In this paper we present a demonstrator application for a real-world m-Health scenario: mobile medical emergency management in an urban area. Medical emergencies have a high priority given the potential life risk to the patients. And the use of advanced applications that support the different actors involved can improve the usage of appropriate resources within an acceptable response time. The demonstrator is implemented using the THOMAS approach to open multiagent system based on an organisational metaphor. This metaphor is very suitable for capturing the nature and the complexity of the mobile health domain and, thus, it provides an appropriate mechanism for developing next-generation m-Health applications.

Designing organized multiagent systems through MDPs

In this paper we present an approach to design an Organized Multiagent Systems (OMAS) for teamwork. We use a general formal model for OMAS that employs the notion of organizational mechanisms. The purpose of such mechanisms is influencing the behaviour of the agents towards more effectiveness with regard to some objectives. To achieve our goal we use Markov Decision Processes (MDPs) as a framework to design the organizational mechanisms. In order to illustrate our approach we use the medical emergencies domain where ambulances have to be selected in order to assist and transport patients to the hospitals.