To my brother

A fundamental problem in designing multiagent systems is to select algorithms that make correct group decisions effectively. Typically, each individual in a group has private, relevant information and making a correct group decision requires that private information be communicated. When there is limited communication bandwidth or potential for delays in communication, it is important to select the algorithm for making group decisions that requires least communication. This thesis makes three contributions to the design of multiagent systems. First, it shows the benefits of quantifying information transmitted by measuring the entropy of messages to find algorithms for decision making that minimize use of bandwidth. Second, it provides an analysis of the information content of a diverse group of center-based algorithms, including several types of auctions, for making group decisions. Third, it defines a new data structure, the dialogue tree, that compactly represents complex interactions between individuals. The thesis demonstrates that the amount of communication required by an algo

Execution monitoring and replanning with incremental and collaborative scheduling

We describe the Flight Manager Assistant (FMA), a prototype system, designed to support real-time management of airlift operations at the USAF Air Mobility Command (AMC). In current practice, AMC flight managers are assigned to manage individual air missions. They tend to be overburdened with associated data monitoring and constraint checking, and generally react to detected problems in a local, myopic fashion. Consequently decisions taken for one mission can often have deleterious effects on others. FMA combines two key capabilities for overcoming these problems: (1) intelligent monitoring of incoming information (for example, weather, airport operations, aircraft status) and recognizing those situations that require corrective action, and (2) dynamic reschedulin

QURATOR: Innovative technologies for content and data curation

In all domains and sectors, the demand for intelligent systems to support the processing and generation of digital content is rapidly increasing. The availability of vast amounts of content and the pressure to publish new content quickly and in rapid succession requires faster, more efficient and smarter processing and generation methods. With a consortium of ten partners from research and industry and a broad range of expertise in AI, Machine Learning and Language Technologies, the QURATOR project, funded by the German Federal Ministry of Education and Research, develops a sustainable and innovative technology platform that provides services to support knowledge workers in various industries to address the challenges they face when curating digital content. The project’s vision and ambition is to establish an ecosystem for content curation technologies that significantly pushes the current state of the art and transforms its region, the metropolitan area Berlin-Brandenburg, into a global centre of excellence for curation technologies