Anthropic agency: a multiagent system for physiological processes

Amigoni, Francesco; Dini, Marco; Gatti, Nicola; Somalvico, Marco

doi:10.1016/s0933-3657(03)00008-3

Cited by 14 publications

(16 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Some basic properties well discussed in the MAS community such as autonomy and coordination [15], are very amenable to the above mentioned problems. MAS has been applied to diabetic healthcare by various researchers in fields such as glucose monitoring and alarm [4,9,10], biomedical control and management in diabetes [1,2,8,11,14].…”

Section: Multi-agent System As a Possible Solutionmentioning

confidence: 99%

Applying Multi-agent Systems Coordination to the Diabetic Healthcare Collaboration

Zhang

Bai²,

Carlsson

et al. 2008

2008 3rd International Conference on Information and Communication Technologies: From Theory to Applications

View full text Add to dashboard Cite

Section: Multi-agent System As a Possible Solutionmentioning

confidence: 99%

Applying Multi-agent Systems Coordination to the Diabetic Healthcare Collaboration

Zhang

Bai²,

Carlsson

et al. 2008

2008 3rd International Conference on Information and Communication Technologies: From Theory to Applications

View full text Add to dashboard Cite

“…Each control action influences a subsystem's behavior in order to obtain a desired effect on the whole system. Significant examples of complex systems that are difficult to control can be found in ad hoc network management [37], vehicle formation [22], market regulation [5,19], and physiological model combination [2,17].…”

Section: Controlling Complex Systemsmentioning

confidence: 99%

“…In market regulation [5,19], the goal is to determine the quantity and the price of the goods produced by the firms in order to maximize the profit. Finally, in controlling physiological phenomena [2,17], the goal is to determine the optimal combination of the control actions produced by a set of partial physiological controllers in order to mimic a physiological function.…”

Section: Controlling Complex Systemsmentioning

confidence: 99%

A formal framework for connective stability of highly decentralized cooperative negotiations

Amigoni

Gatti

2007

Auton Agent Multi-Agent Syst

Self Cite

View full text Add to dashboard Cite

Multiagent cooperative negotiation is a promising technique for modeling and controlling complex systems. Effective and flexible cooperative negotiations are especially useful for open complex systems characterized by high decentralization (which implies a low amount of exchanged information) and by dynamic connection and disconnection of agents. Applications include ad hoc network management, vehicle formation, and physiological model combination. To obtain an effective control action, the stability of the negotiation, namely the guarantee that an agreement will be eventually reached, is of paramount importance. However, the techniques usually employed for assessing the stability of a negotiation can be hardly applied in open scenarios. In this paper, whose nature is mainly theoretical, we make a first attempt towards engineering stable cooperative negotiations proposing a framework for their analysis and design. Specifically, we present a formal protocol for cooperative negotiations between a number of agents and we propose a criterion for negotiation stability based on the concept of connective stability. This is a form of stability that accounts for the effects of structural changes on the composition of a system and that appears very suitable for multiagent cooperative negotiations. To show its possible uses, we apply our framework for connective stability to some negotiations taken from literature.

show abstract

“…The scenario, although extremely realistic, is not yet completely implementable in reality; however, the technological building blocks are already available or being actively developed. To make the discussion more concrete and to partially relate to our previous work [30], we will refer, in the rest of this paper, to another similar application scenario. We consider a diabetic patient equipped with on-body monitoring devices who is in a room equipped with AmI devices.…”

Section: Application Scenariosmentioning

confidence: 99%

“…The goal generator agent stands for any device or user that can generate a goal for the AmI system. Note that this agent might represent (i.e., be connected to) a whole multiagent system devoted to regulate and monitor physiological processes, such as the one we developed in [30]. Hence, the goal generator agent provides the input to our planner in terms of high-level goals to be reached.…”

Section: Application Scenariosmentioning

confidence: 99%

What Planner for Ambient Intelligence Applications?

Amigoni

Gatti

Pinciroli

et al. 2005

IEEE Trans. Syst., Man, Cybern. A

View full text Add to dashboard Cite

Abstract-The development of ambient intelligence (AmI) applications that effectively adapt to the needs of the users and environments requires, among other things, the presence of planning mechanisms for goal-oriented behavior. Planning is intended as the ability of an AmI system to build a course of actions that, when carried out by the devices in the environment, achieve a given goal. The problem of planning in AmI has not yet been adequately explored in literature. In this paper, we propose a planning system for AmI applications, based on the hierarchical task network (HTN) approach and called distributed hierarchical task network (D-HTN), able to find courses of actions to address given goals. The plans produced by D-HTN are flexibly tailored to exploit the capabilities of the devices currently available in the environment in the best way. We discuss both the architecture and the implementation of D-HTN. Moreover, we present some of the experimental results that validated the proposed planner in a realistic application scenario in which an AmI system monitors and answers the needs of a diabetic patient.

show abstract

Anthropic agency: a multiagent system for physiological processes

Cited by 14 publications

References 27 publications

Applying Multi-agent Systems Coordination to the Diabetic Healthcare Collaboration

Applying Multi-agent Systems Coordination to the Diabetic Healthcare Collaboration

A formal framework for connective stability of highly decentralized cooperative negotiations

What Planner for Ambient Intelligence Applications?

Contact Info

Product

Resources

About