Multi-robot path planning is difficult due to the combinatorial explosion of the search space with every new robot added. Complete search of the combined state-space soon becomes intractable. In this paper we present a novel form of abstraction that allows us to plan much more efficiently. The key to this abstraction is the partitioning of the map into subgraphs of known structure with entry and exit restrictions which we can represent compactly. Planning then becomes a search in the much smaller space of subgraph configurations. Once an abstract plan is found, it can be quickly resolved into a correct (but possibly sub-optimal) concrete plan without the need for further search. We prove that this technique is sound and complete and demonstrate its practical effectiveness on a real map.A contending solution, prioritised planning, is also evaluated and shown to have similar performance albeit at the cost of completeness. The two approaches are not necessarily conflicting; we demonstrate how they can be combined into a single algorithm which outperforms either approach alone.

Trust and collective learning are useful features that are enabled by effective collaborative leadership of e-learning projects across higher and further education (HE/FE) institutions promoting lifelong learning. These features contribute effectively to the development of design for learning in communities of e-learning practice. For this, reflexivity, good leadership and the capacity to engage in innovation is crucial to team performance. This paper outlines a serendipitously useful combination of innovative models of collaboration emerging from two 2005-06 UK e-learning pilots: the Joint Information Systems Committee (JISC) e-Learning Independent Study Award (eLISA) and JISC infoNet Collaborative Approaches to the Management of e-Learning (CAMEL) projects. The JISC-funded eLISA Distributed e-Learning (DeL) project set up a collaborative partnership among teachers to try out LAMS and Moodle using study skills in e-learning. Simultaneously, the JISC infoNet CAMEL project developed a model of collaborative approaches to e-learning leadership and management across four UK HE/FE institutions. This paper proposes two new theoretical collaborative team leadership and operational models for elearning projects, including indices of trust, reflexivity and shared procedural knowledge, recommending that these models are further developed in future communities of e-learning practice in institutions promoting lifelong learning.

As robots and Artificial Intelligences become more enmeshed in rich social contexts, it seems inevitable that we will have to make them into moral machines equipped with moral skills. Apart from the technical difficulties of how we could achieve this goal, we can also ask the ethical question of whether we should seek to create such Artificial Moral Agents (AMAs). Recently, several papers have argued that we have strong reasons not to develop AMAs. In response, we develop a comprehensive analysis of the relevant arguments for and against creating AMAs, and we argue that all things considered we have strong reasons to continue to responsibly develop AMAs. The key contributions of this paper are threefold. First, to provide the first comprehensive response to the important arguments made against AMAs by Wynsberghe and Robbins (in "Critiquing the Reasons for Making Artificial Moral Agents", Science and Engineering Ethics 25, 2019) and to introduce several novel lines of argument in the process. Second, to collate and thematise for the first time the key arguments for and against AMAs in a single paper. Third, to recast the debate away from blanket arguments for or against AMAs in general, to a more nuanced discussion about the use of what sort of AMAs, in what sort of contexts, and for what sort of purposes is morally appropriate.

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