Interactive Collaborative Information Systems

The increasing complexity of our world demands new perspectives on the role of technology in decision making. Human decision making has its li- tations in terms of information-processing capacity. 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 tra?c management, where humans need to engage in close collaborations with arti?cial systems to observe and understand the situation and respond in a sensible way. We believe that close collaborations between humans and arti?cial systems will become essential and that the importance of research into Interactive Collaborative Information Systems (ICIS) is self-evident. Developments in information and communication technology have ra- cally changed our working environments. The vast amount of information available nowadays and the wirelessly networked nature of our modern so- ety open up new opportunities to handle di?cult decision-making situations such as computer-supported situation assessment and distributed decision making. To make good use of these new possibilities, we need to update our traditional views on the role and capabilities of information systems. The aim of the Interactive Collaborative Information Systems project is to develop techniques that support humans in complex information en- ronments and that facilitate distributed decision-making capabilities. ICIS emphasizes the importance of building actor-agent communities: close c- laborations between human and arti?cial actors that highlight their comp- mentary capabilities, and in which task distribution is ?exible and adaptive.

Reinforcement Learning.- Approximate Dynamic Programming and Reinforcement Learning.- Learning with Whom to Communicate Using Relational Reinforcement Learning.- Switching between Representations in Reinforcement Learning.- Collaborative Decision Making.- A Decision-Theoretic Approach to Collaboration: Principal Description Methods and Efficient Heuristic Approximations.- Efficient Methods for Near-Optimal Sequential Decision Making under Uncertainty.- Ant Colony Learning Algorithm for Optimal Control.- Map-Based Support for Effective Collaboration in Micro-mobile Virtual Teams.- Computer-Human Interaction Modeling.- Affective Dialogue Management Using Factored POMDPs.- Context-Aware Multimodal Human–Computer Interaction.- Design Issues for Pen-Centric Interactive Maps.- Interacting with Adaptive Systems.- Example-Based Human Pose Recovery under Predicted Partial Occlusions.- Architectures for Distributed Agent-Actor Communities.- Agility and Adaptive Autonomy in Networked Organizations.- Adaptive Hierarchical Multi-agent Organizations.- Method for Designing Networking Adaptive Interactive Hybrid Systems.- Case Studies and Applications.- A Call for Sensemaking Support Systems in Crisis Management.- A Distributed Approach to Gas Detection and Source Localization Using Heterogeneous Information.- Traffic Light Control by Multiagent Reinforcement Learning Systems.- Fusing Heterogeneous and Unreliable Data from Traffic Sensors.- Bayesian Networks for Expert Systems: Theory and Practical Applications.