A CASE FOR DOMAIN-INDEPENDENT DETERMINISTIC MULTIAGENT

The notion of planning using multiple agents has been around since the very beginning of planning itself. It has been approached from various viewpoints especially in the multiagent systems community. Recently, domain-independent multiagent planning has gained more attention also in the automated planning community. In this paper, we shortly present the current state of the art, question some aspects of the research field and discuss the rising challenges

Uninterruptible Power Supply of the Emergency Device

This bachelor project contains a basic description of the emergency device and an introduction to this problem. The project describes uses of NiCd and Pb batteries of this branch. It introduces the basic terms, symbols, types and characteristics of batteries. And it introduces the basic measurement elektric quantity of batteries

Production rules for the project Pogamut 2

The project Pogamut 2 provides a possibility of fast prototyping of agent behaviours in a complex envirorment of the Unreal Tournament 2004 computer game. A fuzzy rule based system was introduced to be used beside the POSH rule based system, which was already a part of the project. Aiming to find out how exactly is it possible to add such system and what possibilities or complications it brings, this thesis presents the theoretical presumptions, their application, a design of an architecture, it's partial implementation and an example of agent controlled by the implemented fuzzy system. The agent's functionality was proved by several experiments. This thesis should also be a basement for furher work, such as full implementation of presented architecture, adding an user interface integrated within Pogamut IDE, and for wider possibilities of experimenting with the fuzzy agents

Production rules for the project Pogamut 2

The project Pogamut 2 provides a possibility of fast prototyping of agent behaviours in a complex envirorment of the Unreal Tournament 2004 computer game. A fuzzy rule based system was introduced to be used beside the POSH rule based system, which was already a part of the project. Aiming to find out how exactly is it possible to add such system and what possibilities or complications it brings, this thesis presents the theoretical presumptions, their application, a design of an architecture, it's partial implementation and an example of agent controlled by the implemented fuzzy system. The agent's functionality was proved by several experiments. This thesis should also be a basement for furher work, such as full implementation of presented architecture, adding an user interface integrated within Pogamut IDE, and for wider possibilities of experimenting with the fuzzy agents

Relaxation Heuristics for Multiagent Planning

Similarly to classical planning, in MA-Strips multiagent planning, heuristics significantly improve efficiency of search-based planners. Heuristics based on solving a relaxation of the original planning problem are intensively studied and well understood. In particular, frequently used is the delete relaxation, where all delete effects of actions are omitted. In this paper, we present a unified view on distribution of delete relaxation heuristics for multiagent planning. Until recently, the most common approach to adaptation of heuristics for multiagent planning was to compute the heuristic estimate using only a projection of the problem for a single agent. In this paper, we place such approach in the context of techniques which allow sharing more information among the agents and thus improve the heuristic estimates. We thoroughly experimentally evaluate properties of our distribution of additive, max and Fast-Forward relaxation heuristics in a planner based on distributed Best-First Search. The best performing distributed relaxation heuristics favorably compares to a state-of-the-art MA-Strips planner in terms of benchmark problem coverage. Finally, we analyze impact of limited agent interactions by means of recursion depth of the heuristic estimates

Potential Heuristics for Multi-Agent Planning

Distributed heuristic search is a well established technique for multi-agent planning. It has been shown that distributed heuristics may crucially improve the search guidance, but are costly in terms of communication and computation time. One solution is to compute a heuristic additively, in the sense that each agent can compute its part of the heuristic independently and obtain a complete heuristic estimate by summing up the individual parts. In this paper, we show that the recently published potential heuristic is a good candidate for such heuristic, moreover admissible. We also demonstrate how the multi-agent distributed A* search can be modified in order to benefit from such additive heuristic. The modified search equipped with a distributed potential heuristic outperforms the state of the art

Privacy Leakage of Search-Based Multi-Agent Planning Algorithms

Privacy-Preserving Multi-Agent Planning (PP-MAP) has recently gained the attention of the research community, resulting in a number of PP-MAP planners and theoretical works. Many such planners lack strong theoretical guarantees, thus in order to compare their abilities w.r.t. privacy, a versatile and practical metric is crucial. In this work, we propose such a metric, building on the existing theoretical work. We generalize and implement the approach in order to be applicable on real planning domains and provide an evaluation of stateof-the-art PP-MAP planners over the standard set of benchmarks. The evaluation shows that the proposed privacy leakage metric is able to provide a comparison of PP-MAP planners and reveal important properties

The Limits of Strong Privacy Preserving Multi-Agent Planning

Multi-agent planning using MA-STRIPS-related models is often motivated by the preservation of private information. Such motivation is not only natural for multi-agent systems, but it is one of the main reasons, why multi-agent planning (MAP) problems cannot be solved centrally. In this paper, we analyze privacy-preserving multi-agent planning (PP-MAP) from the perspective of secure multiparty computation (MPC). We discuss the concept of strong privacy and its implications and present two variants of a novel planner, provably strong privacy-preserving in general. As the main contribution, we formulate the limits of strong privacy-preserving planning in the terms of privacy, completeness and efficiency and show that, for a wide class of planning algorithms, all three properties are not achievable at once. Moreover, we provide a restricted variant of strong privacy based on equivalence classes of planning problems and show that an efficient, complete and strong privacy-preserving planner exists for such restriction

Competition of Distributed and Multiagent Planners (CoDMAP)

As a part of the workshop on Distributed and Multiagent Planning (DMAP) at the International Conference on Automated Planning and Scheduling (ICAPS) 2015, we have organized a competition in distributed and multiagent planning. The main aims of the competition were to consolidate the planners in terms of input format; to promote development of multiagent planners both inside and outside of the multiagent research community; and to provide a proof-of-concept of a potential future multiagent planning track of the International Planning Competition (IPC). In this paper we summarize course and highlights of the competition

Admissible Landmark Heuristic for Multi-Agent Planning

Heuristics are a crucial component in modern planning systems. In optimal multiagent planning the state of the art is to compute the heuristic locally using only information available to a single agent. This approach has a major deficiency as the local shortest path can arbitrarily underestimate the true shortest path cost in the global problem. As a solution, we propose a distributed version of a state-of-the-art LM-Cut heuristic. We show that our distributed algorithm provides estimates provably equal to estimates of the centralized version computed on the global problem. We also evaluate the algorithm experimentally and show that on a number of domains, the distributed algorithm can significantly improve performance of a multiagent planner