New governance for innovation : the need for horizontal and systemic policy co-ordination ; report on a workshop

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Specification of vertical semantic consistency rules of UML class diagram refinement using logical approach

Unified Modelling Language (UML) is the most popular modelling language use for software design in software development industries with a class diagram being the most frequently use diagram. Despite the popularity of UML, it is being affected by inconsistency problems of its diagrams at the same or different abstraction levels. Inconsistency in UML is mostly caused by existence of various views on the same system and sometimes leads to potentially conflicting system specifications. In general, syntactic consistency can be automatically checked and therefore is supported by current UML Computer-aided Software Engineering (CASE) tools. Semantic consistency problems, unlike syntactic consistency problems, there exists no specific method for specifying semantic consistency rules and constraints. Therefore, this research has specified twenty-four abstraction rules of class‟s relation semantic among any three related classes of a refined class diagram to semantically equivalent relations of two of the classes using a logical approach. This research has also formalized three vertical semantic consistency rules of a class diagram refinement identified by previous researchers using a logical approach and a set of formalized abstraction rules. The results were successfully evaluated using hotel management system and passenger list system case studies and were found to be reliable and efficient

Born Under a Lucky Star?

This paper suggests that people can learn to behave in a way which makes them unlucky or lucky. Learning from experience will lead them to make choices which may lead to "luckier" outcomes than others. By so doing they may reinforce the choices of those who find themselves with unlucky outcomes. In this situation, people have reasonably learned to behave as they do and their behaviour is consistent with their experience. The lucky ones were not "born under a lucky star" they learned to be lucky.

Objective Styles in Northern Field Science

Social studies of science have often treated natural field sites as extensions of the laboratory. But this overlooks the unique specificities of field sites. While lab sites are usually private spaces with carefully controlled borders, field sites are more typically public spaces with fluid boundaries and diverse inhabitants. Field scientists must therefore often adapt their work to the demands and interests of local agents. I propose to address the difference between lab and field in sociological terms, as a difference in style. A field style treats epistemic alterity as a resource rather than an obstacle for objective knowledge production. A sociological stylistics of the field should thus explain how objective science can co-exist with radical conceptual difference. I discuss examples from the Canadian North, focussing on collaborations between state wildlife biologists and managers, on the one hand, and local Aboriginal Elders and hunters, on the other. I argue that a sociological stylistics of the field can help us to better understand how radically diverse agents may collaborate across cultures in the successful production of reliable natural knowledge

Human-Machine Collaborative Optimization via Apprenticeship Scheduling

Coordinating agents to complete a set of tasks with intercoupled temporal and resource constraints is computationally challenging, yet human domain experts can solve these difficult scheduling problems using paradigms learned through years of apprenticeship. A process for manually codifying this domain knowledge within a computational framework is necessary to scale beyond the ``single-expert, single-trainee" apprenticeship model. However, human domain experts often have difficulty describing their decision-making processes, causing the codification of this knowledge to become laborious. We propose a new approach for capturing domain-expert heuristics through a pairwise ranking formulation. Our approach is model-free and does not require enumerating or iterating through a large state space. We empirically demonstrate that this approach accurately learns multifaceted heuristics on a synthetic data set incorporating job-shop scheduling and vehicle routing problems, as well as on two real-world data sets consisting of demonstrations of experts solving a weapon-to-target assignment problem and a hospital resource allocation problem. We also demonstrate that policies learned from human scheduling demonstration via apprenticeship learning can substantially improve the efficiency of a branch-and-bound search for an optimal schedule. We employ this human-machine collaborative optimization technique on a variant of the weapon-to-target assignment problem. We demonstrate that this technique generates solutions substantially superior to those produced by human domain experts at a rate up to 9.5 times faster than an optimization approach and can be applied to optimally solve problems twice as complex as those solved by a human demonstrator.Comment: Portions of this paper were published in the Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI) in 2016 and in the Proceedings of Robotics: Science and Systems (RSS) in 2016. The paper consists of 50 pages with 11 figures and 4 table

Highways, market access and spatial sorting

We design a spatial model featuring workers embodied with heterogeneous skills. In equilibrium, locations with improved market access become relatively more attractive to the high-skilled, high-income earners. We then empirically analyze the effects of the construction of the Swiss highway network between 1960 and 2010 on the distribution of income at the local level, as well as on employment and commuting by education level. We find that the advent of a new highway access within 10km led to a long-term 19%-increase of the share of high-income taxpayers and a 6%-decrease of the share of low-income taxpayers. Results are similar for employment data decomposed by education level, as well as for in- and outcommuters. Highways also contributed to job and residential urban spraw

Re-conceptualising the Language Game Paradigm in the Framework of Multi-Agent Reinforcement Learning

In this paper, we formulate the challenge of re-conceptualising the language game experimental paradigm in the framework of multi-agent reinforcement learning (MARL). If successful, future language game experiments will benefit from the rapid and promising methodological advances in the MARL community, while future MARL experiments on learning emergent communication will benefit from the insights and results gained from language game experiments. We strongly believe that this cross-pollination has the potential to lead to major breakthroughs in the modelling of how human-like languages can emerge and evolve in multi-agent systems.Comment: This paper was accepted for presentation at the 2020 AAAI Spring Symposium `Challenges and Opportunities for Multi-Agent Reinforcement Learning' after a double-blind reviewing proces