Artificial Intelligence Research: The Octopus Algorithm for Generating Goal-Directed Feedback

Artificial Intelligence Research: The Octopus Algorithm for Generating Goal-Directed FeedbackCharles XieBlockedUnblockFollowFollowingDec 2Originally published on July 1, 2018 at BloggerDuring the 2010 FIFA World Cup eight years ago, a common octopus named Paul the Octopus gained worldwide attention because it accurately “predicted” all the results of the most important soccer matches in the world (sadly it died by natural courses shortly after that)..Through three months of intense work in this field, I have developed genetic algorithms that can be used to find optimal solutions in complex design environments such as the Energy3D CAD software, which you can find in earlier articles published through my blog..1: An illustration of the Octopus AlgorithmHence, an optimization algorithm that yields the best solution in a long single run is not very useful to developers and educators as it doesn’t provide sufficient opportunities for engaging students..This kind of algorithm is known as local search, a technique for finding an optimal solution in the vicinity of a starting point that represents the learner’s current state (as opposed to global search that casts a wide net across the entire solution space, representing equally all possibilities regardless of the learner’s current state)..Random optimization is one of the local search methods proposed in 1965, which stochastically generates a set of candidate solutions distributed around the initial solution in accordance with the normal distribution..Once an optimum is “felt” (i.e., one or more solution points close to the optimum is included in the randomly generated population of the genetic algorithm), the “octopus” will move towards it (i.e., the best solution from the population will converge to the optimum) as driven by the genetic algorithm..I call this particular combination of random optimization and genetic algorithm the Octopus Algorithm as it intuitively mimics how an octopus hunts on the sea floor (and, in part, to honor Paul the Octopus and to celebrate the 2018 World Cup Tournament)..It just stayed there at the end of the fourth run.When there are multiple optima in the solution space (a problem known as multimodal optimization), it may be appropriate to expect that AI would guide students to the nearest optimum..With a conventional genetic algorithm that performs global search with uniform initial selection across the solution space, there is simply no guarantee that the suggested solution would take the student’s current solution into consideration, even though his/her current solution can be included as part of the first generation (which, by the way, may be quickly discarded if the solution turns out to be a bad one)..4: Using four “octopuses” to locate four optimal orientations for the energy efficiency of a house.Let’s see how the Octopus Algorithm finds multiple optima..The ability for the algorithm to detect the three other optimal solutions simultaneously, known as multi-niche optimization, may not be essential.Fig..A “fatter octopus” may be problematic.In summary, the Octopus Algorithm that I have invented seems to be able to accurately guide a designer to the nearest optimal solution in an engineering design process7.Unlike Paul the Octopus that relied on supernatural forces (or did it?), the Octopus Algorithm is an AI technique that we create, control, and leverage.. More details

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