No abstract

If a solvable problem is currently unsolved, then something important to a solution is most likely being overlooked. From this simple observation we derive the obscure features hypothesis: every innovative solution is built upon at least one commonly overlooked or new (i.e., obscure) feature of the problem. By using a new definition of a feature as an effect of an interaction, we are able to accomplish five things. First, we are able to determine where features come from and how to search for new ones. Second, we are able to construct mathematical arguments that the set of features of an object is not computably enumerable. Third, we are able to characterize innovative problem solving as looking for a series of interactions that produce the desired effects (i.e., the goal). Fourth, we are able to construct a precise problem-solving grammar that is both human and machine friendly. Fifth, we are able to devise a visual and verbal problem-solving representation that both humans and computers can contribute to as they help counteract each other's problem-solving weaknesses. We show how computers can counter some of the known cognitive obstacles to innovation that humans have. We also briefly discuss ways in which humans can return the favor. We conclude that a promising process for innovative problem solving is a humancomputer collaboration in which each partner assists the other in unearthing the obscure features of a problem.

A recent analysis of real-world problems that led to historic inventions and insight problems that are used in psychology experiments suggests that during innovative problem solving, individuals discover at least one infrequently noticed or new (i.e., obscure) feature of the problem that can be used to reach a solution. This observation suggests that research uncovering aspects of the human semantic, perceptual, and motor systems that inhibit the noticing of obscure features would enable researchers to identify effective techniques to overcome those obstacles. As a critical step in this research program, this study showed that the generic-parts technique can help people unearth the types of obscure features that can be used to overcome functional fixedness, which is a classic inhibitor to problem solving. Subjects trained on this technique solved on average 67% more problems than a control group did. By devising techniques that facilitate the noticing of obscure features in order to overcome impediments to problem solving (e.g., design fixation), researchers can systematically create a tool kit of innovation-enhancing techniques.

In order to maximize creative behavior, humans and computers need to collaborate in a manner that will leverage the strengths of both. A 2017 mathematical proof shows two limits to how innovative a computer can be. Humans can help counteract these demonstrated limits. Humans possess many mental blind spots to innovating (e.g., functional fixedness, design fixation, analogy blindness, etc.), and particular algorithms can help counteract these shortcomings. Further, since humans produce the corpora used by AI technology, human blind spots to innovation are implicit within the text processed by AI technology. Known algorithms that query humans in particular ways can effectively counter these text-based blind spots. Working together, a human-computer partnership can achieve higher degrees of innovation than either working alone. To become an effective partnership, however, a special interface is needed that is both human-and computer-friendly. This interface called BrainSwarming possesses a linguistic component, which is a formal grammar that is also natural for humans to use and a visual component that is easily represented by standard data structures. Further, the interface breaks down innovative problem solving into its essential components: a goal, sub-goals, resources, features, interactions, and effects. The resulting human-AI synergy has the potential to achieve innovative breakthroughs that either partner working alone may never achieve.

Analysis of over 1,000 innovative inventions reveals that during the innovative process at least one rarely-noticed or new (i.e., obscure) feature is unearthed and built upon to create the solution (i.e., the Obscure Features Hypothesis for innovation: OFH) [6,7]. Embedding the insights from this analysis into the structure of semantic networks creates AhaNets, which help optimize the search for the needed key obscure feature. Techniques to overcome cognitive aversions to noticing the obscure (i.e., fixation effects) further enhance innovation by improving the search process. Once implemented in software, AhaNets and counter-fixation techniques create an innovation-enhancing human-machine interaction.