Gregor Pavlin

A multi-agent systems approach to distributed bayesian information fusion

This paper introduces design principles for modular Bayesian fusion systems which can (i) cope with large quantities of heterogeneous information and (ii) can adapt to changing constellations of information sources on the fly. The presented approach exploits the locality of relations in causal probabilistic processes, which facilitates decentralized modeling and information fusion. Observed events resulting from stochastic causal processes can be modeled with the help of Bayesian networks, compact and mathematically rigorous probabilistic models. With the help of the theory of Bayesian networks and factor graphs we derive design and organization rules for modular fusion systems which implement exact belief propagation without centralized configuration and fusion control. These rules are applied in distributed perception networks (DPN), a multi-agent systems approach to distributed Bayesian information fusion. While each DPN agent has limited fusion capabilities, multiple DPN agents can autonomously collaborate to form complex modular fusion systems. Such self-organizing systems of agents can adapt to the available information sources at runtime and can infer critical hidden events through interpretation of complex patterns consisting of many heterogeneous observations.

Inference Meta Models: Towards Robust Information Fusion with Bayesian Networks

This paper discusses the properties of Bayesian networks (BNs) in the context of accurate state estimation. We focus on a relevant class of problems where state estimation can be viewed as a classification of possible states based on the fusion of heterogeneous and noisy information. We introduce the inference meta model (IMM), a coarse runtime perspective on the inference processes which facilitates the analysis of the state estimation with BNs. By making coarse and realistic assumptions, we show that such inference can be very robust and has asymptotic properties regarding the fusion accuracy, even if we use models and evidence associated with significant uncertainties. Moreover, the IMM provides guidance for the development of (i) robust fusion systems and (ii) methods for runtime detection of potentially misleading fusion results

Real World Multi-agent Systems: Information Sharing, Coordination and Planning

Applying multi-agent systems in real world scenarios requires several essential research questions to be answered. Agents have to perceive their environment in order to take useful actions. In a multi-agent system this results in a distributed perception of partial information, which has to be fused. Based on the perceived environment the agents have to plan and coordinate their actions. The relation between action and perception, which forms the basis for planning, can be learned by perceiving the result of an action. In this paper we focus these three major research questions. First, we investigate distributed world models that describe the aspects of the world that are relevant for the problem at hand. Distributed Perception Networks are introduced to fuse observations to obtain robust and efficient situation assessments. Second, we show how coordination graphs can be applied to multi-robot teams to allow for efficient coordination.Third, we present techniques for agent planning in uncertain environments, in which the agent only receives partial information (through its sensors) regarding the true state of environment.

A MAS Approach to Fusion of Heterogeneous Information

Distributed perception networks (DPN) are a MAS approach to large scale fusion of heterogeneous and noisy information. DPN agents can establish meaningful information filtering channels between the relevant information sources and the decision makers. Through specification of high level concepts, DPN agent organizations generate distributed Bayesian networks, which provide mappings between the observed symptoms and the hypotheses relevant to the decision making. In addition, DPNs support robust distributed inference as well as decentralized probabilistic resource allocation.

DIADEM: a system for collaborative environmental monitoring

Environmental monitoring and emergency response projects in urban-industrial areas increasingly rely on efficient collaboration between experts in control rooms and at incident locations, and citizens who live or work in the area. In the video accompanying this abstract we present a system that uses distributed sensor technology, Bayesian decision tools, and advanced map-based interfaces to facilitate collaboration between environmental experts and the public for environmental monitoring and early detection of chemical incidents.

A Collaborative Approach to Construction of Complex Service Oriented Systems

This paper introduces a new collaborative approach to the construction of large scale service oriented systems using a combination of light weight service ontologies, efficient construction procedures and tools. In particular, machine-understandable descriptions of heterogeneous services with well defined syntax and semantics can be created by multiple designers, without complex coordination of collaborative design processes and without any knowledge of formal ontologies.

Design and Implementation of a Service Negotiation Framework for Collaborative Disaster Management Applications

We present the design and implementation of a generic framework for cooperative multi-issue one-to-many negotiations for optimal service provisioning in collaborative disaster management information systems. The framework allows us to define negotiation protocols, negotiation subjects, properties of negotiation subject issues, deal spaces, and utility functions of participant agents. This framework was integrated into a service oriented architecture for complex collaborative processing in distributed information systems.

A Distributed Approach to Gas Detection and Source Localization Using Heterogeneous Information

This chapter introduces a system for early detection of gaseous substances and coarse source localization by using heterogeneous sensor measurements and human reports. The system is based on Distributed Perception Networks, a Multi-agent system framework implementing distributed Bayesian reasoning. Causal probabilistic models are exploited in several complementary ways. They support uniform and efficient integration of very heterogeneous information sources, such as different static and mobile sensors as well as human reports. In principle, modular Bayesian networks allow creation of complex probabilistic observation models which adapt to changing constellations of information sources at runtime. On the other hand, Bayesian networks are used also for coarse modeling of transitions in the gas propagation processes. By combining dynamic models of gas propagation processes with the observation models, we obtain adaptive Bayesian systems which correspond to Hidden Markov Models. The resulting systems facilitate seamless combination of prior domain knowledge and heterogeneous observations.

'Do you smell rotten eggs?': evaluating interactions with mobile agents in crisis response situations

In this paper, we present ongoing research concerning the interaction between users and autonomous mobile agents in the environmental monitoring domain. The overarching project, DIADEM, deals with developing a system that detects potentially hazardous situations in populated industrial areas using input from both a distributed sensor network and humans through mobile devices. We propose a model of interaction with the gas detection system where concerned citizens communicate with a mobile agent to inform the gas monitoring system about unusual smells via their mobile phones. Next, we present a preliminary user requirements analysis based on 40 phone calls from members of the public to an environmental monitoring agency. Finally, we introduce measures to study the delicate long-term social relationship between users and the gas monitoring system.

Using Negotiation for Dynamic Composition of Services in Multi-organizational Environmental Management

This paper discusses an efficient solution to contemporary situation assessment problems found in environmental management applications. The targeted problems are inherently complex and require processing of large quantities of heterogeneous information by using rich domain knowledge dispersed through multiple organizations. We assume a collaborative solution based on the Dynamic Process Integration Framework, which supports systematic encapsulation of heterogeneous processing services, including human experts. The encapsulation allows dynamic composition of heterogeneous processing services using advanced self-configuration mechanisms for optimal selection of service providers. Self-configuration is based on a framework for development of cooperative multi-issue one-to-many service negotiations. The framework allows the definition of: negotiation protocols, negotiation subjects composed of multiple negotiation issues, properties of negotiation issues, deal spaces, and utility functions of participant stakeholders. We show how this framework can be used for dynamic composition of workflows spanning multiple organizations in a disaster management information system.