Conference Paper

Side-by-side Human-Computer Design using a Tangible User Interface

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Abstract

We present a digital-physical system to support side-by-side collabora-tive human-computer design exploration. The system consists of a sensor-instrumented "sand table" functioning as a digital-tangible space for exploring early-stage design decisions. Using our system, the human designer generates phyiscal representations of design solutions, while monitoring a visu-alization of the solutions objective space. At the same time, the AI system uses the vicinity of the humans exploration point to continuously seed its search algorithms and suggest design alternatives. We present an experimental study comparing this side-by-side design space exploration to human-only design exploration and to AI-only optimization. We find that side-by-side collaboration of a human and a computer significantly improves design outcomes and offers benefits in terms of the user experience. However, side-by-side human-computer design also leads to more narrow design space exploration and to less diverse solutions when compared to both human-only and computer-only search. This has important implications for future human-computer collaborative design systems.

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... Prior work in human-in-the-loop design typically frames the process by either having the human evaluate designs proposed by the agent, e.g. as the fitness function in an interactive genetic algorithm [14,5,11], or having the agent find satisficing solutions to a set of goals and constraints defined by the human designer [44]. In some work, agents make suggestions to the human based on user exploration [3,30]. ...
... The TUI on which the human and robot explore their designs is based on the reacTIVision ReacTable [24] and our prior work [30]. Passive blocks, each tagged on the underside with a fiducial marker, are used to represent different design components. ...
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This paper presents a review of the R&D literature on CSCD, from the pre-CSCD technologies of the 1980's to today's state-of-the-art CSCD. Research challenges and opportunities on CSCD are also discussed and highlighted. Dans ce document, on présente une analyse bibliographique de la documentation portant sur la R et D dans le domaine de la conception collaborative assistée par ordinateur (CCAO), depuis les technologies antérieures à la CCAO des années 1980 jusqu'à la CCAO d'aujourd'hui, à la fine pointe de la technologie. En outre, on fait ressortir les défis et les opportunités de recherches dans le domaine, qui font ici l'objet d'une discussion. RES
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This article for the layman answers basic questions about artificial intelligence.
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Over the past few years, the research on evolutionary algorithms has demonstrated their niche in solving multiobjective optimization problems, where the goal is to find a number of Pareto-optimal solutions in a single simulation run. Many studies have depicted different ways evolutionary algorithms can progress towards the Pareto-optimal set with a widely spread distribution of solutions. However, none of the multiobjective evolutionary algorithms (MOEAs) has a proof of convergence to the true Pareto-optimal solutions with a wide diversity among the solutions. In this paper, we discuss why a number of earlier MOEAs do not have such properties. Based on the concept of epsilon-dominance, new archiving strategies are proposed that overcome this fundamental problem and provably lead to MOEAs that have both the desired convergence and distribution properties. A number of modifications to the baseline algorithm are also suggested. The concept of epsilon-dominance introduced in this paper is practical and should make the proposed algorithms useful to researchers and practitioners alike.
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Multi-objective evolutionary algorithms (MOEAs) that use non-dominated sorting and sharing have been criticized mainly for: (1) their O(MN3) computational complexity (where M is the number of objectives and N is the population size); (2) their non-elitism approach; and (3) the need to specify a sharing parameter. In this paper, we suggest a non-dominated sorting-based MOEA, called NSGA-II (Non-dominated Sorting Genetic Algorithm II), which alleviates all of the above three difficulties. Specifically, a fast non-dominated sorting approach with O(MN2) computational complexity is presented. Also, a selection operator is presented that creates a mating pool by combining the parent and offspring populations and selecting the best N solutions (with respect to fitness and spread). Simulation results on difficult test problems show that NSGA-II is able, for most problems, to find a much better spread of solutions and better convergence near the true Pareto-optimal front compared to the Pareto-archived evolution strategy and the strength-Pareto evolutionary algorithm - two other elitist MOEAs that pay special attention to creating a diverse Pareto-optimal front. Moreover, we modify the definition of dominance in order to solve constrained multi-objective problems efficiently. Simulation results of the constrained NSGA-II on a number of test problems, including a five-objective, seven-constraint nonlinear problem, are compared with another constrained multi-objective optimizer, and the much better performance of NSGA-II is observed
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We discuss what constitutes an integrated system in AI, and why AI researchers should be interested in building and studying them. Taking integrated systems to be ones that integrate a variety of components in order to perform some task from start to finish, we believe that such systems (a) allow us to better ground our theoretical work in actual tasks, and (b) provide an opportunity for much-needed evaluation based on task performance. We describe one particular integrated system we have developed that supports spoken-language dialogue to collaboratively solve planning problems. We discuss how the integrated system provides key advantages for helping both our work in natural language dialogue processing and in interactive planning and problem solving, and consider the opportunities such an approach affords for the future. Content areas: AI systems, natural language understanding, planning and control, problem solving, user interfaces Introduction It is an interesting time to be an A...
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