Machines incorporating techniques from artificial intelligence and machine learning can work with human users on a moment-to-moment, real-time basis to generate creative outcomes, performances and artefacts. We define such systems collaborative, creative AI systems, and in this article, consider the theoretical and practical considerations needed for their design so as to support improvisation, performance and co-creation through real-time, sustained, moment-to-moment interaction. We begin by providing an overview of creative AI systems, examining strengths, opportunities and criticisms in order to draw out the key considerations when designing AI for human creative collaboration. We argue that the artistic goals and creative process should be first and foremost in any design. We then draw from a range of research that looks at human collaboration and teamwork, to examine features that support trust, cooperation, shared awareness and a shared information space. We highlight the importance of understanding the scope and perception of two-way communication between human and machine agents in order to support reflection on conflict, error, evaluation and flow. We conclude with a summary of the range of design challenges for building such systems in provoking, challenging and enhancing human creative activity through their creative agency.
Refinement is a powerful mechanism for mastering the complexities that arise when formally modelling systems. Refinement also brings with it additional proof obligations—requiring a developer to discover properties relating to their design decisions. With the goal of reducing this burden, we have investigated how a general purpose automated theory formation tool, HR, can be used to automate the discovery of such properties within the context of the Event-B formal modelling framework. This gave rise to an integrated approach to automated invariant discovery. In addition to formal modelling and automated theory formation, our approach relies upon the simulation of system models as a key input to the invariant discovery process. Moreover we have developed a set of heuristics which, when coupled with automated proof-failure analysis, have enabled us to effectively tailor HR to the needs of Event-B developments. Drawing in part upon case study material from the literature, we have achieved some promising experimental results. While our focus has been on Event-B, we believe that our approach could be applied more widely to formal modelling frameworks which support simulation.
One current direction in AI research is to focus on combining different reasoning styles such as deduction, induction, abduction, analogical reasoning, non-monotonic reasoning, vague and uncertain reasoning. The philosopher Imre Lakatos produced one such theory of how people with different reasoning styles collaborate to develop mathematical ideas. Lakatos argued that mathematics is a quasi-empirical, flexible, fallible, human endeavour, involving negotiations, mistakes, vague concept definitions and disagreements, and he outlined a heuristic approach towards the subject. In this chapter we apply these heuristics to the AI domains of evolving requirement specifications, planning and constraint satisfaction problems. In drawing analogies between Lakatos’s theory and these three domains we identify areas of work which correspond to each heuristic, and suggest extensions and further ways in which Lakatos’s philosophy can inform AI problem solving. Thus, we show how we might begin to produce a philosophically-inspired AI theory of combined reasoning.
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