Solving dynamic controllability problem of multi-agent plans with uncertainty using mixed integer linear programming.

Executing multi-agent missions requires managing the uncertainty about uncontrollable events. When communications are intermittent, it additionally requires for each agent to act only based on its local view of the problem, that is independently of events which are controlled or observed by the other agents. In this paper, we propose a new framework for dealing with such contexts, with a focus on mission plans involving temporal constraints. This framework, called Multi-agent Simple Temporal Network with Uncertainty (MaSTNU), is a combination between Multi-agent Simple Temporal Network (MaSTN) and Simple Temporal Network with Uncertainty (STNU).We define the dynamic controllability property for MaSTNU, and a method for computing offline valid execution strategies which are then dispatched between agents. This method is based on a mixed-integer linear programming formulation and can also be used to optimize criteria such as the temporal flexibility of multi-agent plans.

HOMEBOTS: Intelligent Decentralized Services for Energy Management

The deregulation of the European energy market, combined with emerging advanced capabilities of information technology, provides strategic opportunities for new knowledge-oriented services on the power grid. HOMEBOTS is the namewe have coined for one of these innovative services: decentralized power load management at the customer side, automatically carried out by a `society' of interactive household, industrial and utility equipment. They act as independent intelligent agents that communicate and negotiate in a computational market economy. The knowledge and competence aspects of this application are discussed, using an improved \ud version of task analysis according to the COMMONKADS knowledge methodology. Illustrated by simulation results, we indicate how customer knowledge can be mobilized to achieve joint goals of cost and energy savings. General implications for knowledge creation and its management are discussed

Planning & Scheduling Applications in Urban Traffic Management

Local authorities that manage traffic-related issues in urban areas have to optimise the use of available resources, in order to minimise congestion and delays. In this context, Automated Planning and Scheduling can be fruitfully exploited, in order to provide dynamic plans that help managing the urban road network. In this paper we provide a review of existing planning and scheduling approaches that have been designed for dealing with different aspects of traffic management, with the aim of gaining insights on the limits of current applications, and highlighting the open challenges

Towards a Framework for Developing Mobile Agents for Managing Distributed Information Resources

Distributed information management tools allow users to author, disseminate, discover and manage information within large-scale networked environments, such as the Internet. Agent technology provides the flexibility and scalability necessary to develop such distributed information management applications. We present a layered organisation that is shared by the specific applications that we build. Within this organisation we describe an architecture where mobile agents can move across distributed environments, integrate with local resources and other mobile agents, and communicate their results back to the user

Automated Negotiation for Provisioning Virtual Private Networks Using FIPA-Compliant Agents

This paper describes the design and implementation of negotiating agents for the task of provisioning virtual private networks. The agents and their interactions comply with the FIPA specification and they are implemented using the FIPA-OS agent framework. Particular attention is focused on the design and implementation of the negotiation algorithms

Dynamic deployment of context-aware access control policies for constrained security devices

Securing the access to a server, guaranteeing a certain level of protection over an encrypted communication channel, executing particular counter measures when attacks are detected are examples of security requirements. Such requirements are identi ed based on organizational purposes and expectations in terms of resource access and availability and also on system vulnerabilities and threats. All these requirements belong to the so-called security policy. Deploying the policy means enforcing, i.e., con guring, those security components and mechanisms so that the system behavior be nally the one speci ed by the policy. The deployment issue becomes more di cult as the growing organizational requirements and expectations generally leave behind the integration of new security functionalities in the information system: the information system will not always embed the necessary security functionalities for the proper deployment of contextual security requirements. To overcome this issue, our solution is based on a central entity approach which takes in charge unmanaged contextual requirements and dynamically redeploys the policy when context changes are detected by this central entity. We also present an improvement over the OrBAC (Organization-Based Access Control) model. Up to now, a controller based on a contextual OrBAC policy is passive, in the sense that it assumes policy evaluation triggered by access requests. Therefore, it does not allow reasoning about policy state evolution when actions occur. The modi cations introduced by our work overcome this limitation and provide a proactive version of the model by integrating concepts from action speci cation languages

Robotic ubiquitous cognitive ecology for smart homes

Robotic ecologies are networks of heterogeneous robotic devices pervasively embedded in everyday environments, where they cooperate to perform complex tasks. While their potential makes them increasingly popular, one fundamental problem is how to make them both autonomous and adaptive, so as to reduce the amount of preparation, pre-programming and human supervision that they require in real world applications. The project RUBICON develops learning solutions which yield cheaper, adaptive and efficient coordination of robotic ecologies. The approach we pursue builds upon a unique combination of methods from cognitive robotics, machine learning, planning and agent- based control, and wireless sensor networks. This paper illustrates the innovations advanced by RUBICON in each of these fronts before describing how the resulting techniques have been integrated and applied to a smart home scenario. The resulting system is able to provide useful services and pro-actively assist the users in their activities. RUBICON learns through an incremental and progressive approach driven by the feed- back received from its own activities and from the user, while also self-organizing the manner in which it uses available sensors, actuators and other functional components in the process. This paper summarises some of the lessons learned by adopting such an approach and outlines promising directions for future work