ANGELINA is an automated game design system which has previously been built as a single software block which designs games from start to finish. In this paper we outline a roadmap for the development of a new version of ANGELINA, designed to iterate on games in different ways to produce a continuous creative process that will improve the quality of its work, but more importantly improve the perception of the software as being an independently creative piece of software. We provide an initial report of the system's structure here as well as results from the first working module of the system.
There are also a range of tools that, while not built as autonomous designers, are sufficiently close in nature that they might only be a few small changes from being such. Tools like @cite_10 use a lot of automation and self-analysis to provide support to a human user, or the @cite_7 which amplifies smaller creative inputs with embellishments and analysis, and @cite_11 , a similar tool targeting physics-based mobile games like . These tools are performing a lot of the design work going on in their domain, even if the project's intention is not to fully automate the task. Often, the need to support humans in any part of a creative tasks means the system must, by definition, be able to perform every part of the task on its own. Such systems are usually only lacking in some higher-level control and production, and are otherwise very close to automated game design tools.
@cite_10 @cite_7 @cite_11
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Game-design novices increasingly hope to use game-like expression as a way to express content such as opinions and educational material. Existing game-design toolkits such as Game Maker ease the programming burden, bringing the design of small games within the technical reach of low-budget, non-expert groups. The design process itself remains a roadblock, however: It is not at all obvious how to present topics such as political viewpoints or bike safety in a way that makes use of the unique qualities of the interactive game medium. There are no tools to assist in that aspect of the game design process, and as a result virtually all expressive games come from a small number of game studios founded by experts in designing such games. We propose a game-design assistant that acts in a mixed-initiative fashion, helping the author understand the content of her design-in-progress, providing suggestions or automating the process where possible, and even offering the possibility for parts of the game to be dynamically generated at runtime in response to player interaction. We describe a prototype system that interactively helps authors define spaces of games in terms of common-sense constraints on their real-world references, provides support for them to understand and iteratively refine such spaces, and realizes specific games from the spaces as playable mobile-phone games in response to user input. This paper reports on the system design for integrating the various processes needed for end-to-end implementation of a smart assistive robotic manipulator. Specifically, progress is reported in the empowerment of the UCF-MANUS system with a suite of sensory, computational, and multimodal interface capabilities so that its autonomy can be made accessible to users with a wide range of disabilities. Laboratory experiments are reported to demonstrate the ability of the system prototype to successfully and efficiently complete object retrieval tasks. Benchmarking of the impact of the various interface modalities on user performance is performed via empirical studies with healthy subjects operating the robot in a simulated instrumental activities of daily living tasks setup. It is seen through a analysis of the collected quantitative data that the prototype is interface neutral and shows robustness to variations in the tasks and the environment. It is also seen that the prototype autonomous system is quantitatively superior to Cartesian control for all tested tasks under a “number of commands” metric, however, under a “time to task completion” metric, the system is seen to be superior for “hard” tasks but not for “easy” tasks. I l l u S t r a t I o n b y a l I C I a k u b I S t a a n D r I J b o r y S a S S o C I a t e S in This arTiCle we consider the question: How should autonomous systems be analyzed? in particular, we describe how the confluence of developments in two areas—autonomous systems architectures and formal verification for rational agents—can provide the basis for the formal verification of autonomous systems behaviors. We discuss an approach to this question that involves: 1. Modeling the behavior and describing the interface (input output) to an agent in charge of making decisions within the system; 2. Model checking the agent within an unrestricted environment representing the “real world” and those parts of the systems external to the agent, in order to establish some property, j; 3. Utilizing theorems or analysis of the environment, in the form of logical statements (where necessary), to derive properties of the larger system; and 4. if the agent is refined, modify (1), but if environmental properties are clarified, modify (3). Autonomous systems are now being deployed in safety, mission, or business critical scenarios, which means a thorough analysis of the choices the core software might make becomes crucial. But, should the analysis and verification of autonomous software be treated any differently than traditional software used in critical situations? Or is there something new going on here? Autonomous systems are systems that decide for themselves what to do and when to do it. Such systems might seem futuristic, but they are closer than we might think. Modern household, business, and industrial systems increasingly incorporate autonomy. There are many examples, all varying in the degree of autonomy used, from almost pure human control to fully autonomous activities with minimal human interaction. Application areas are broad, ranging from healthcare monitoring to autonomous vehicles. But what are the reasons for this increase in autonomy? Typically, autonomy is used in systems that: 1. must be deployed in remote environments where direct human control is infeasible; 2. must be deployed in hostile environments where it is dangerous for humans to be nearby, and so difficult for humans to assess the possibilities; 3. involve activity that is too lengthy Verifying autonomous systems Doi:10.1145 2494558 One subclass of human computation applications are those directed at tasks that involve planning (e.g. tour planning) and scheduling (e.g. conference scheduling). Interestingly, work on these systems shows that even primitive forms of automated oversight on the human contributors helps in significantly improving the effectiveness of the humans crowd. In this paper, we argue that the automated oversight used in these systems can be viewed as a primitive automated planner, and that there are several opportunities for more sophisticated automated planning in effectively steering the crowd. Straightforward adaptation of current planning technology is however hampered by the mismatch between the capabilities of human workers and automated planners. We identify and partially address two important challenges that need to be overcome before such adaptation of planning technology can occur: (i) interpreting inputs of the human workers (and the requester) and (ii) steering or critiquing plans produced by the human workers, armed only with incomplete domain and preference models. To these ends, we describe the implementation of AI-MIX, a tour plan generation system that uses automated checks and alerts to improve the quality of plans created by human workers; and present a preliminary evaluation of the effectiveness of steering provided by automated planning.