Antonín Komenda

Agent-Based Multi-Layer Collision Avoidance to Unmanned Aerial Vehicles

This contribution presents a distributed, multi-layer collision avoidance architecture supporting efficient utilization of air space shared by several autonomous aerial vehicles. Presented multi-layer architecture is based on deliberative deployment of several collision avoidance methods by the aircraft at the same time. Both cooperative and non-cooperative collision avoidance methods are presented in the paper. The robustness of the architecture is justified by means of experimental validation of multi-agent simulation

Developing Multiagent Algorithms for Tactical Missions Using Simulation

The development process and simulation architecture presented here help narrow the gap between how theoretical AI algorithms are traditionally designed and validated and how practical algorithms for controlling robotic assets in simulated tactical missions are developed.

Abstract Architecture for Task-oriented Multi-agent Problem Solving

Problem solving and planning in decentralized environments is a key technical challenge in numerous industrial applications, ranging from manufacturing, logistics, virtual enterprizes to multirobotics systems. We present an abstract architecture of a multiagent solver and respective algorithm providing decomposition, task allocation, and task delegation. Various features of the abstract architecture, such as computational complexity or admissibility of the underlying optimization heuristics, are analyzed in the paper. Four instances of the abstract architecture implementations are given to demonstrate the applicability of the abstract solver in a wide variety of real-problem domains.

Mobility Model for Tactical Networks

In this paper a synthetic mobility model which represents behavior and movement pattern of heterogeneous units in disaster relief and battlefield scenarios is proposed. These operations usually take place in environment without preexisting communication infrastructure and units thus have to be connected by wireless communication network. Units cooperate to fulfill common tasks and communication network has to serve high amount of communication requests, especially data, voice and video stream transmissions. To verify features of topology control, routing and interaction protocols software simulations are usually used, because of their scalability, repeatability and speed. Behavior of all these protocols relies on the mobility model of the network nodes, which has to resemble real-life movement pattern. Proposed mobility model is goal-driven and provides support for various types of units, group mobility and realistic environment model with obstacles. Basic characteristics of the mobility model like node spatial distribution and average node degree were analyzed.

Domain-independent multi-agent plan repair

Achieving joint objectives in distributed domain-independent planning problems by teams of cooperative agents requires significant coordination and communication efforts. For systems facing a plan failure in a dynamic environment, arguably, attempts to repair the failed plan in general, and especially in the worst-case scenarios, do not straightforwardly bring any benefit in terms of time complexity. However, in multi-agent settings, the communication complexity might be of a much higher importance, possibly a high communication overhead might be even prohibitive in certain domains. We hypothesize that in decentralized systems, where frequent coordination is required to achieve joint objectives, attempts to repair failed multi-agent plans should lead to lower communication overhead than replanning from scratch. Here, we formally introduce the multi-agent plan repair problem. Building upon the formal treatment, we present the core hypothesis underlying our work and subsequently describe three algorithms for multi-agent plan repair reducing the problem to specialized instances of the multi-agent planning problem. Finally, we present an experimental validation, results of which confirm the core hypothesis of the paper. Our rigorous treatment of the problem and experimental results pave the way for both further analytical, as well algorithmic investigations of the problem.

Privacy-concerned multiagent planning

Coordinated sequential decision making of a team of cooperative agents can be described by principles of multiagent planning. Provided that the mechanics of the environment the agents act in is described as a deterministic transitions system, an appropriate planning model is MA-Strips. Multiagent planning modeled as MA-Strips prescribes exactly what information has to be kept private and which information can be communicated in order to coordinate toward shared or individual goals. We propose a multiagent planning approach which combines compilation for a classical state-of-the-art planner together with a compact representation of local plans in the form of finite-state machines. Proving soundness and completeness of the approach, the planner efficiency is further boosted up using distributed delete-relaxation heuristics and using an approximative local plan analysis. We experimentally evaluate applicability of our approach in full privacy setting where only public information can be communicated. We analyze properties of standard multiagent benchmarks from the perspective of classification of private and public information. We show that our approach can be used with different privacy settings and that it outperforms state-of-the-art planners designed directly for particular privacy classification.

Planning and re-planning in multi-actors scenarios by means of social commitments

We present an approach to plan representation in multi-actors scenarios that is suitable for flexible replanning and plan revision purposes. The key idea of the presented approach is in integration of (i) the results of an arbitrary HTN (hierarchical task network) -oriented planner with (ii) the concept of commitments, as a theoretically studied formalism representing mutual relations among intentions of collaborating agents. The paper presents formal model of recursive form of commitments and discusses how it can be deployed to a selected hierarchical planning scenario.

Cooperative Multi-Agent Planning: A Survey

Cooperative multi-agent planning (MAP) is a relatively recent research field that combines technologies, algorithms, and techniques developed by the Artificial Intelligence Planning and Multi-Agent Systems communities. While planning has been generally treated as a single-agent task, MAP generalizes this concept by considering multiple intelligent agents that work cooperatively to develop a course of action that satisfies the goals of the group. This article reviews the most relevant approaches to MAP, putting the focus on the solvers that took part in the 2015 Competition of Distributed and Multi-Agent Planning, and classifies them according to their key features and relative performance.

The International Competition of Distributed and Multiagent Planners (CoDMAP)

This article reports on the first international Competition of Distributed and Multiagent Planners (CoDMAP). The competition focused on cooperative domain-independent planners compatible with a minimal multiagent extension of the classical planning model. The motivations for the competition were manifold: to standardize the problem description language with a common set of benchmarks, to promote development of multiagent planners both inside and outside of the multiagent research community, and to serve as a prototype for future multiagent planning competitions. The article provides an overview of cooperative multiagent planning, describes a novel variant of standardized input language for encoding mutliagent planning problems and summarizes the key points of organization, competing planners and results of the competition.

Ground tactical mission support by multi-agent control of UAV operations

Autonomous control of group of unmanned aerial vehicles based on task allocation mechanisms shows great potential for ground tactical mission support. We introduce experimental simulation system combining flexible mission control of ground assets in urban environment and autonomous aerial support utilizing multi-agent problem solving techniques. Two case-studies are presented for evaluation - cooperative area surveillance and dynamic target tracking with undervalued number of assets. We show the strength and benefits of multi-agent task allocation and delegation mechanisms in such dynamic scenarios mainly in case of limited number of assets.