Michal Pechoucek

Autonomous UAV Surveillance in Complex Urban Environments

We address the problem of multi-UAV-based surveillance in complex urban environments with occlusions. The problem consists of controlling the flight of UAVs with on-board cameras so that the coverage and recency of the information about the designated area is maximized. In contrast to the existing work, sensing constraints due to occlusions and UAV motion constraints are modeled realistically and taken into account. We propose a novel \emph{occlusion-aware} surveillance algorithm based on a decomposition of the surveillance problem into a variant of the 3D art gallery problem and an instance of traveling salesman problem for Dubins vehicles. The algorithm is evaluated on the high-fidelity \textsc{AgentFly} UAV simulation testbed which accurately models all constraints and effects involved. The results confirm the importance of occlusion-aware flight path planning, in particular in the case of narrow-street areas and low UAV flight altitudes.

A knowledge-based approach to coalition formation

Establishing coalitions among humanitarian aid organizations is a challenge. The CPlanT (Coalition Planning Tool) system uses a multi-agent knowledge-based approach to reduce complexity and communication traffic, while also protecting stakeholder privacy.

Adaptive Multiagent System for Network Traffic Monitoring

Individual anomaly-detection methods for monitoring computer network traffic have relatively high error rates. An agent-based trust-modeling system fuses anomaly data and progressively improves classification to achieve acceptable error rates.

Agent-Based Approach to Free-Flight Planning, Control, and Simulation

Intelligent-agents technology provides techniques and algorithms for distributed coordination and distributed decision making. The authors developed AgentFly, a multiagent prototype for air traffic control of free-flight-based operations of multiple aerial assets, based on intelligent agents. AgentFly provides mechanisms for distributed planning, negotiation-based collision avoidance, and multiagent flight simulation. The US Air Force supports this project, but the Federal Aviation Administration is also studying AgentFly for planning mixed traffic of manned and unmanned air traffic.

ExPlanTech: multiagent support for manufacturing decision making

ExPlanTechs multiagent approach offers a unified framework for decision-making support and provides a proven alternative to known mathematical and system science-modeling technologies for simulating the manufacturing process. ExPlanTech provides technological support for various manufacturing problems and comprises different components, which you can assemble to develop a customized system that supports a users decision making in different aspects of production planning. The system should help human users size resources and time requirements for a particular order, creating production plans, optimizing material resources manipulation, managing and optimizing supply chain relationships, visualizing and analyzing medium- and long-term manufacturing processes, and accessing external data.

Agent-Based Cooperative Decentralized Airplane-Collision Avoidance

The efficiency of the current centralized air-traffic management is limited. A next-generation air transportation system should allow airplanes (manned and unmanned) to change their flight paths during the flight without approval from a centralized en route control. Such a scheme requires decentralized peer-to-peer conflict detection and collision-avoidance processes. In this paper, two cooperative (negotiation-based) conflict-resolution algorithms are presented: iterative peer-to-peer and multiparty algorithms. They are based on high-level flight-plan variations using evasion maneuvers. The algorithms work with a different level of coordination autonomy, respect realistic assumptions of imprecise flight execution (integrating required navigation performance), and work in real time, where the planning and plan-execution phases interleave. Both algorithms provide a resolution in a 4-D domain (3-D space and time). The proposed algorithms are evaluated experimentally, and their quality is studied in comparison with a state-of-the-art agent-based method-the satisficing game theory algorithm.

Representing Context for Multiagent Trust Modeling

We present a universal mechanism that can be combined with existing trust models to extend their capabilities towards efficient modelling of the situational (context- dependent) trust. The mechanism describes the similarity between the situations using their distance in a metric space and defines a set of reference contexts in this space to which it associates the trustfulness data. The data associated with each reference context is updated and queried with the weight that decreases with distance between the current situation and the reference context. In the presented mechanism, we use Leader-Follower clustering to place the reference contexts to be representative of the data. In an empirical test, we show that context-aware models easily outperform the general trust when the situation has an impact on partner trustfulness and that their performance and efficiency is comparable with general trust models when the trustfulness is independent of the situation. Multi-context nature of the model also expands its use towards more advanced uses, allowing policy/norm learning from at the trust model at runtime, as well as reasoning based on uncertain identities.

Software agents for process monitoring and notification

Safety and efficiency are primary concerns in chemical processing facilities, though the complexity of many such systems often makes it difficult for operators to detect abnormal conditions before they compromise throughput or become hazardous. In this paper, we report initial results from the application of multi-agent systems to monitor complex chemical processes and flexibly and appropriately notify key plant personnel about off-nominal conditions.

Trust model for open ubiquitous agent systems

Trust management model that we present is adapted for ubiquitous devices cooperation, rather than for classic client-supplier relationship. We use fuzzy numbers to represent trust, to capture both the trust value and its uncertainty. The model contains the trust representation part, decision-making part and a learning part. In our representation, we define the trusted agents as a type-2 fuzzy set. In a decision-making part, we use the methods from the fuzzy rule computation and fuzzy control domain to take trusting decision. For trust learning, we use a strictly iterative approach, well adapted to constrained environments. We verify our model in a multi-agent simulation where the agents in the community learn to identify defecting members and progressively refuse to cooperate with them. Our simulation contains significant background noise to validate model robustness.

Transiting areas patrolled by a mobile adversary

We study the problem of a mobile agent trying to cross an area patrolled by a mobile adversary. The transiting agent aims to choose its route so as to minimize the probability of hostile encounter; the patroller agent, controlling one or more patrol units, aims at the opposite. We model the problem as a two-player zero-sum game (termed transit game) and search for an optimum route selection strategy as a mixed Nash equilibrium of the game. In contrast to existing game-theoretic models of this kind, we explicitly consider the limited endurance of patrols and the notion of bases to which the patrols need to repeatedly return. Noting the prohibitive size of the transit game, we employ two techniques for reducing the complexity of finding Nash equilibria - a compact network-flow-based representation of transit routes and iterative single- and double-oracle algorithms for incremental game matrix construction. We measure the computational time of all the methods on a range of transit game instances. In order to assess the practical relevance of the approach, we apply the transit game model and its solution to the real-world case of ship transit through areas affected by piracy. The results obtained using an agent-based simulation of maritime traffic show that the randomized game-theoretic transit routing strategy results in a lower number of pirate attacks than the currently employed method based on static transit corridors.