Abstract: This chapter gives an overview of the state of the art in decision-theoretic models to describe cooperation between multiple agents in a dynamic environment.
Making (near-) optimal decisions in such settings gets harder when the number of agents grows or the uncertainty about the environment increases. It is essential to have compact models, because otherwise just representing the decision problem
becomes intractable. Several such model descriptions and approximate solution methods, studied in the Interactive Collaborative Information Systems project, are presented and illustrated in the context of crisis management.
Abstract: The investigations presented in this thesis are part of the 'Integrated Collaborative Information Systems' (ICIS) project, focussing on the 'Enhanced Situation Awareness' (ESA). As a partner in ths project, we investigated the feasibility of using morphologicallt elaborate model neurons to enhance robustness and adaptivity in robotic systems.
Abstract: In this article we consider the issue of optimal control in collaborative multi-agent systems with stochastic dynamics. The agents have a joint task in which they have to reach a number of target states. The dynamics of the agents contains additive control and additive noise, and the autonomous part factorizes over the agents. Full observation of the global state is assumed. The goal is to minimize the accumulated joint cost, which consists of integrated instantaneous costs and a joint end cost. The joint end cost expresses the joint task of the agents. The instantaneous costs are quadratic in the control and factorize over the agents. The optimal control is given as a weighted linear combination of single-agent to single-target controls. The single-agent to single-target controls are expressed in terms of diffusion processes. These controls, when not closed form expressions, are formulated in terms of path integrals, which are calculated approximately by Metropolis-Hastings sampling. The weights in the control are interpreted as marginals of a joint distribution over agent to target assignments. The structure of the latter is represented by a graphical model, and the marginals are obtained by graphical model inference. Exact inference of the graphical model will break down in large systems, and so approximate inference methods are needed. We use naive mean field approximation and belief propagation to approximate the optimal control in systems with linear dynamics. We compare the approximate inference methods with the exact solution, and we show that they can accurately compute the optimal control. Finally, we demonstrate the control method in multi-agent systems with nonlinear dynamics consisting of up to 80 agents that have to reach an equal number of target states.
Abstract: In times of major disasters such as hurricane Katrina or the Sichuan earthquake, the need for accurate and timely information is as crucial as is rapid and coherent coordination among the responding humanitarian community. Effective humanitarian information systems that provide timely access to comprehensive, relevant, and reliable information are critical to humanitarian operations. The faster the humanitarian community is able to collect, analyze, disseminate and act on key information, the more effective the response will be, the better needs will be met, and the greater the benefit to the affected populations. This paper presents fundamental principles of humanitarian information management as endorsed by the international humanitarian community, introduces generic systems design premises and presents two recent collaborative efforts in humanitarian information systems development.
Abstract: In actor-agent teams human and artificial entities interact and cooperate in order to enhance and augment their individual and joint cognitive ergonomic and problem solving capabilities. Also actor-agent communities can benefit from ‘ambient cognition’, a novel further reaching concept than ambient intelligence that hardly takes into account the resource limitations and capabilities changing over time of both humans and agents in collaborative settings. The Dutch Companion project aims at the
realization of an agent that takes advantage of the ambient cognition concerning actor-agent system dynamics such that natural emotion-sensitive interaction with an actor over a longer period of time can be sustained. We elaborate on our vision of
pursuing ambient cognition within actor-agent systems and present the plans and expected results of the Dutch Companion project.
Abstract: In actor-agent teams human and artiﬁcial entities interact and cooperate in order to enhance and augment their individual and joint cognitive ergonomic and problem solving capabilities. Also actor-agent communities can beneﬁt from ‘ambient cognition’, a novel further reaching concept than ambient intelligence that hardly takes into account the resource limitations and capabilities changing over time of both humans and agents in collaborative settings. The Dutch Companion project aims at the realization of an agent that takes advantage of the ambient cognition concerning actor-agent system dynamics such that natural social and emotion-sensitive interaction with an actor over a longer period of time can be sustained. We elaborate on our vision of pursuing ambient cognition within actor-agent systems and brieﬂy describe the goals of the Dutch Companion project.
Abstract: This research stems from the MOSAIC project, a part of the valorization and knowledge transfer effort of
the Interactive Collaborative Information Systems (ICIS) research programme (http://www.icis.decis.nl/),
supported by the Dutch Ministry of Economic Affairs, grant no.: BSIK03024. ICIS is hosted by the D-CIS
Lab (http://www.decis.nl/), the open research partnership of Thales Nederland, the Delft University of
Technology, the University of Amsterdam and the Netherlands Organization for Applied Scientific
Abstract: The increasing complexity of our world demands new perspectives on the role of technology in human decision making. We need new technology to cope with the increasingly complex and information-rich nature of our modern society. This is particularly true for critical environments such as crisis management and traffic management, where humans need to engage in close collaborations with artificial systems to observe and understand the situation and respond in a sensible way. The book Interactive Collaborative Information Systems addresses techniques that support humans in situations in which complex information handling is required and that facilitate distributed decision-making. The theme integrates research from information technology, artificial intelligence and human sciences to obtain a multidisciplinary foundation from which innovative actor-agent systems for critical environments can emerge. It emphasizes the importance of building actor-agent communities: close collaborations between human and artificial actors that highlight their complementary capabilities in situations where task distribution is flexible and adaptive. This book focuses on the employment of innovative agent technology, advanced machine learning techniques, and cognition-based interface technology for the use in collaborative decision support systems.
Abstract: The proliferation of small mobile devices and wireless networks has resulted in an increasing demand to support the applications found in wired environments on mobile devices. In real time replication systems, such as collaborativesystems, this trend gives some new problems to address. The properties of wireless networks are low bandwidth and high latency, which change dynamically over time. The risk of the network getting congested is therefore high with the result that the user will not receive the important information in time. Consequently there is a need to develop algorithms and methods for adaptive work environments and adaptive data distribution, to minimise the traffic load. An architecture based on multi- and mobile-agents is proposed as a solution. Personalized behaviour is included in a flexible and extensible system. A prototype of the architecture has been implemented in a crisis environment and was used for an evaluation. It is assumed that each individual in the field is equipped with a PDA that can communicate with other PDA's in the surrounding and remote servers. Users can report about their environment using a personalized iconic language. Each user is supervised by a personal agent. In case of emergency users are routed outside a dangerous area using a personalised dynamic routing system, called PIRA "Personal Intelligent Routing Assistant". The system and results of testing will be presented in this paper.
Abstract: Recently, a theory for stochastic optimal control in non-linear dynamical systems in continuous space-time has been developed (Kappen, 2005). We apply this theory to collaborative multi-agent systems. The agents
evolve according to a given non-linear dynamics with additive Wiener noise. Each
agent can control its own dynamics. The goal
is to minimize the accumulated joint cost,
which consists of a state dependent term and
a term that is quadratic in the control. We focus on systems of non-interacting agents that
have to distribute themselves optimally over
a number of targets, given a set of end-costs
for the different possible agent-target combinations. We show that optimal control is
the combinatorial sum of independent single-
agent single-target optimal controls weighted
by a factor proportional to the end-costs
of the different combinations. Thus, multi-
agent control is related to a standard graphical model inference problem. The additional
computational cost compared to single-agent
control is exponential in the tree-width of the
graph specifying the combinatorial sum times
the number of targets. We illustrate the result by simulations of systems with up to 42
Abstract: This report is the first of four documents, which describe the distributed tasking architecture that
has been developed by Thales Research and Technology (UK) Ltd. This architecture has been
developed to test generic tasking and re-tasking policies as part of the Tasking and Re-tasking
sub-project for the Interactive Collaborative Information Systems (ICIS) programme. This report offers: 1) An overview of the entire architecture, focusing on those components that are common
to both distributed and centralised tasking processes
2) Information on the Java services required to run the tasking architecture.
Abstract: This report is the second of four documents, which describe the distributed tasking
architecture that has been developed by Thales Research and Technology (UK) Ltd.
This architecture has been developed to test generic tasking and re-tasking policies as
part of the Tasking and Re-tasking sub-project for the Interactive Collaborative
Information Systems (ICIS) programme. This second document focuses on how objects and interfaces have been implemented
to enable centralised tasking algorithms processes to be utilised in the tasking
architecture. Centralised tasking is a traditional, top down approach to tasking whereby
a centralised actor or agent has access to all information about tasks and resources and
is responsible for making all task allocations.
Abstract: Conducting empirical research involves a balancing act between scientific rigor and real-life pragmatics. The Delft Co-operation on Intelligent Systems (D-CIS) laboratory researches systems-of-systems consisting of the human and artificial systems involved in collaborative decision-making under chaotic circumstances. An important objective is the usefulness of our results in our major application domain: crisis management. The D-CIS lab was involved in setting up a crisis management exercise experiment and the according measurements regarding an improvement in internal communication at Gemeente (Municipality) Borsele. In this paper, the empirical research regarding this experiment, the methodology and its results are briefly outlined. The main lessons learned concern the interrelationship between the scenario, experiment and measurements, the problem of acquiring usable data and the challenges of conducting grounded research.
Abstract: Conducting empirical research involves a balancing act between scientific rigor and real-life pragmatics. DECIS Lab researches systems-of-systems, consisting of humans and artificial systems involved in collaborative decision making under chaotic circumstances. An important objective is the usefulness of our results to our major application domain: crisis management. DECIS Lab was involved to set up a crisis management exercise experiment and according measurements regarding an improvement in internal communication at Gemeente (Municipality) Borsele. In this paper the empirical research regarding this experiment, the methodology and its results are briefly outlined. Our main lessons learned concern the interrelationship between scenario, experiment and measurements; the problem of acquiring usable data; and the challenges of conducting grounded research.
Abstract: This paper illustrates objectives of the DIADEM project which focuses on a
novel combination of advanced technologies which facilitate collaborative information
processing in environmental management applications. The emphasis is on the principles
and tools which facilitate creation of a single information space in advanced systems of
systems through a systematic integration of heterogeneous processes. In particular, we
illustrate the main principles of the DIADEM Process Integration framework, which
supports collaborative processing based on a combination of automated reasoning
processes and cognitive capabilities of multiple human experts, each contributing specific
expertise and processing resources.