Michal Štolba

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.

Quantifying Privacy Leakage in Multi-Agent Planning

Multi-agent planning using MA-STRIPS–related models is often motivated by the preservation of private information. Such a motivation is not only natural for multi-agent systems but also is one of the main reasons multi-agent planning problems cannot be solved with a centralized approach. Although the motivation is common in the literature, the formal treatment of privacy is often missing. In this article, we expand on a privacy measure based on information leakage introduced in previous work, where the leaked information is measured in terms of transition systems represented by the public part of the problem with regard to the information obtained during the planning process. Moreover, we present a general approach to computing privacy leakage of search-based multi-agent planners by utilizing search-tree reconstruction and classification of leaked superfluous information about the applicability of actions. Finally, we present an analysis of the privacy leakage of two well-known algorithms—multi-agent forward search (MAFS) and Secure-MAFS—both in general and on a particular example. The results of the analysis show that Secure-MAFS leaks less information than MAFS.

The MADLA planner: Multi-agent planning by combination of distributed and local heuristic search

Abstract Real world applications often require cooperation of multiple independent entities. Classical planning is a well established technique solving various challenging problems such as logistic planning, factory process planning, military mission planning and high-level planning for robots. Multi-agent planning aims at solving similar problems in the presence of multiple independent entities (agents). Even though such entities might want to cooperate in order to fulfill a common goal, they may want to keep their internal information and processes private. In such case, we talk about privacy-preserving multi-agent planning. So far, multi-agent planners based on heuristic search used either a local heuristic estimating the particular agents local subproblem or a distributed heuristic estimating the global problem as a whole. In this paper, we present the Multi-Agent Distributed and Local Asynchronous (MADLA) Planner, running a novel variant of a distributed state-space forward-chaining multi-heuristic search which combines the use of a local and a distributed heuristic in order to combine their benefits. In particular, the planner uses two variants of the well known Fast-Forward heuristic. We provide proofs of soundness and completeness of the search algorithm and show how much and what type of privacy it preserves. We also provide an improved privacy-preserving distribution scheme for the Fast-Forward heuristic. We experimentally compare the newly proposed multi-heuristic scheme and the two used heuristics separately. The results show that the proposed solution outperforms classical (single-heuristic) distributed search with either one of the heuristics used separately. In the detailed experimental analysis, we show limits of the planner and of the used heuristics based on particular properties of the benchmark domains. In a comprehensive set of multi-agent planning domains and problems, we show that the MADLA Planner outperforms all contemporary state-of-the-art privacy-preserving multi-agent planners using a compatible planning model.

Secure 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 is one of the main reasons, why multi-agent planning problems cannot be solved centrally. Although the motivation is common in the literature, formal treatment of privacy is mostly missing. An exception is a definition of two extreme concepts, weak and strong privacy. In this paper, we first analyze privacy leakage in the terms of secure Multi-Party Computation and Quantitative Information Flow. Then, we follow by analyzing privacy leakage of the most common MAP paradigms. Finally, we propose a new theoretical class of secure MAP algorithms and show how the existing techniques can be modified in order to fall in the proposed class.

Seamless Electromobility

The existing electromobility (EM) is still in its fledgling stage and multiple challenges have to be overcome to make Electric Vehicles (EVs) as convenient as combustion engine vehicles. Users and Electric Vehicle Fleet Operators (EFOs) want their EVs to be charged and ready for use at all times. This straightforward goal, however, is counteracted from various sides: The range of the EV depends on the status and depletion of the EV battery which is influenced by EV use and charging characteristics. Also, most convenient charging from the users point of view, might unfortunately lead to problems in the power grid. As in the case of a power peak in the evening when EV users return from work and simultaneously plug in their EVs for charging. Last but not least, the mass of EV batteries are an untapped potential to store electricity from intermittent renewable energy sources. In this paper, we propose a novel approach to tackle this multi-layered problem from different perspectives. Using on-board EV data and grid prediction models, we build up an information model as a foundation for a back end service containing EFO and Charging Station Provider (CSP) logic as well as a central Advanced Drivers Assistant System (ADAS). These components connect to both battery management and user interfaces suggesting various routing and driving behaviour alternatives customized and incentivized for the current user profile optimizing above mentioned goals.

ε-Strong Privacy Preserving Multiagent Planner by Computational Tractability.

Classical planning can solve large and real-world problems, even when multiple entities, such as robots, trucks or companies, are concerned. But when the interested parties, such as cooperating companies, are interested in maintaining their privacy while planning, classical planning cannot be used. Although, privacy is one of the crucial aspects of multi-agent planning, studies of privacy are underepresented in the literature. A strong privacy property, necessary to leak no information at all, has not been achieved by any planner in general yet. In this contribution, we propose a multiagent planner which can get arbitrarily close to the general strong privacy preserving planner for the price of decreased planning efficiency. The strong privacy assurances are under computational tractability assumptions commonly used in secure computation research.

Whole Day Mobility Planning with Electric Vehicles.

We propose a novel and challenging variant of trip planning problems – Whole Day Mobility Planning with Electric Vehicles (WDMEV). WDMEV combines several concerns, which has been so far only considered separately, in order to realistically model the problem of planning mobility with electric vehicles (EVs). A key difference between trip planning for combustion engine cars and trip planning for EVs is the comparatively lower battery capacity and comparatively long charging times of EVs – which makes it important to carefully consider charging when planning travel. The key idea behind WDMEV is that the user can better optimize his/her mobility with EVs, if it considers the activities he/she needs to perform and the travel required to get to the locations of these activities for the whole day rather than planning for single trips only. In this paper, we formalize the WDMEV problem and propose a solution based on a label-setting heuristic search algorithm, including several speed-ups. We evaluate the proposed algorithm on a realistic set of benchmark problems, confirming that the whole day approach reduces the time required to complete one’s day travel with EVs and that it also makes it cheaper, compared to the traditional single-trip approach.

\({\upvarepsilon }\)-Strong Privacy Preserving Multi-agent Planning

Multi-agent planning can solve various sequential decision problems comprising multiple entities. In contrast to classical planning, the agents are interested in maintaining privacy while planning with each other. Therefore they have to reason about what information they can share. Although privacy is one of the crucial aspects of multi-agent planning, formal and algorithmic treatment of privacy is rather sparse in literature. No domain-independent strong privacy preserving multi-agent planner was proposed so far. Moreover, our recent results indicate that an efficient variant of such planner may not exist at all. Such strong privacy preserving planner would not allow to leak any private information during planning neither directly nor indirectly. Especially the indirect leakage is hard to assess as it can be based on any possible deduction principle from the non-private information along the planning process.

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.