Mixed Initiative Systems for Human-Swarm Interaction: Opportunities and Challenges

Human-swarm interaction (HSI) involves a number of human factors impacting human behaviour throughout the interaction. As the technologies used within HSI advance, it is more tempting to increase the level of swarm autonomy within the interaction to reduce the workload on humans. Yet, the prospective negative effects of high levels of autonomy on human situational awareness can hinder this process. Flexible autonomy aims at trading-off these effects by changing the level of autonomy within the interaction when required; with mixed-initiatives combining human preferences and automation's recommendations to select an appropriate level of autonomy at a certain point of time. However, the effective implementation of mixed-initiative systems raises fundamental questions on how to combine human preferences and automation recommendations, how to realise the selected level of autonomy, and what the future impacts on the cognitive states of a human are. We explore open challenges that hamper the process of developing effective flexible autonomy. We then highlight the potential benefits of using system modelling techniques in HSI by illustrating how they provide HSI designers with an opportunity to evaluate different strategies for assessing the state of the mission and for adapting the level of autonomy within the interaction to maximise mission success metrics.Comment: Author version, accepted at the 2018 IEEE Annual Systems Modelling Conference, Canberra, Australi

Towards formal models and languages for verifiable Multi-Robot Systems

Incorrect operations of a Multi-Robot System (MRS) may not only lead to unsatisfactory results, but can also cause economic losses and threats to safety. These threats may not always be apparent, since they may arise as unforeseen consequences of the interactions between elements of the system. This call for tools and techniques that can help in providing guarantees about MRSs behaviour. We think that, whenever possible, these guarantees should be backed up by formal proofs to complement traditional approaches based on testing and simulation. We believe that tailored linguistic support to specify MRSs is a major step towards this goal. In particular, reducing the gap between typical features of an MRS and the level of abstraction of the linguistic primitives would simplify both the specification of these systems and the verification of their properties. In this work, we review different agent-oriented languages and their features; we then consider a selection of case studies of interest and implement them useing the surveyed languages. We also evaluate and compare effectiveness of the proposed solution, considering, in particular, easiness of expressing non-trivial behaviour.Comment: Changed formattin

Decentralized Control of Partially Observable Markov Decision Processes using Belief Space Macro-actions

The focus of this paper is on solving multi-robot planning problems in continuous spaces with partial observability. Decentralized partially observable Markov decision processes (Dec-POMDPs) are general models for multi-robot coordination problems, but representing and solving Dec-POMDPs is often intractable for large problems. To allow for a high-level representation that is natural for multi-robot problems and scalable to large discrete and continuous problems, this paper extends the Dec-POMDP model to the decentralized partially observable semi-Markov decision process (Dec-POSMDP). The Dec-POSMDP formulation allows asynchronous decision-making by the robots, which is crucial in multi-robot domains. We also present an algorithm for solving this Dec-POSMDP which is much more scalable than previous methods since it can incorporate closed-loop belief space macro-actions in planning. These macro-actions are automatically constructed to produce robust solutions. The proposed method's performance is evaluated on a complex multi-robot package delivery problem under uncertainty, showing that our approach can naturally represent multi-robot problems and provide high-quality solutions for large-scale problems

Building the Core Architecture of a Multiagent System Product Line: With an example from a future NASA Mission

The field of Software Product Lines (SPL) emphasizes building a core architecture for a family of software products from which concrete products can be derived rapidly. This helps to reduce time-to-market, costs, etc., and can result in improved software quality and safety. Current AOSE methodologies are concerned with developing a single Multiagent System. We propose an initial approach to developing the core architecture of a Multiagent Systems Product Line (MAS-PL), exemplifying our approach with reference to a concept NASA mission based on multiagent technology

Building the Core Architecture of a NASA Multiagent System Product Line

The field of Software Product Lines (SPL) emphasizes build- ing a family of software products from which concrete products can be derived rapidly. This helps to reduce time-to-market, costs, etc., and can result in improved software quality and safety. Current Agent-Oriented Software Engineering (AOSE) methodologies are concerned with devel- oping a single Multiagent System. The main contribution of this paper is a proposal to developing the core architecture of a Multiagent Systems Product Line (MAS-PL), exemplifying our approach with reference to a concept NASA mission based on multiagent technology

Building the Core Architecture of a NASAa Multiagent System Product Line

The field of Software Product Lines (SPL) emphasizes building a family of software products from which concrete products can be derived rapidly. This helps to reduce time-to-market, costs, etc., and can result in improved software quality and safety. Current Agent-Oriented Software Engineering (AOSE) methodologies are concerned with developing a single Multiagent System. The main contribution of this paper is a proposal to developing the core architecture of a Multiagent Systems Product Line (MAS-PL), exemplifying our approach with reference to a concept NASA mission based on multiagent technology