Causally-Guided Evolutionary Computation for Design

During recent years, evolutionary computation methods have been used successfully to discover solutions to problems involving design and invention in a wide variety of fields. However, for the evolutionary process to remain computationally tractable when applied to increasingly complex design problems, new extensions must be developed that increase the efficiency and effectiveness with which evolutionary systems produce optimal designs. To this end, the goal of the research presented here is to develop one such potential extension: causally-guided evolution. By this I mean evolutionary systems where the application of genetic operators to an individual are driven in part by observing that individual's performance characteristics and applying these operators based on explicit cause-effect relations in the domain. This differs from past evolutionary methods in which, after fitness-based selection, genetic operators are applied to individuals blindly and randomly (i.e., without respect to the performance characteristics of the individuals). In this context, this dissertation makes a number of significant contributions. A framework for causally-guided evolution is defined, including causally-guided genetic operators based on causal knowledge that is supplied by domain experts. The ability of these methods and causally-guided mutation to produce better solutions than conventional evolutionary processes is demonstrated on a neural network optimization task. These methods are then extended to include crossover, and the synergistic effects of causally-guided crossover and mutation are demonstrated when applied to a real-world antenna design task. Causally-guided mutation is extended further to influence both where and how mutation occurs, and the effectiveness of this approach is shown when applied to a constructive design task that creates synthetic social networks. Finally, a causally-guided evolutionary system that acquires causal knowledge through observation of the evolutionary process, rather than being given the knowledge a priori, is developed and successfully applied, demonstrating the applicability of causally-guided evolution to problems in which causal knowledge is not available. Collectively, this work clearly demonstrates for the first time the promise of causally-guided evolutionary computation in a variety of forms and when applied to a range of application problems

Integrating Knowledge-Based and Case-Based Reasoning

There has been substantial recent interest in integrating knowledge based reasoning (KBR) and case-based reasoning (CBR) within a single system due to the potential synergisms that could result. Here we describe our recent work investigating the feasibility of a combined KBR-CBR application-independent system for interpreting multi-episode stories/narratives, illustrating it with an application in the domain of interpreting urban warfare stories. A genetic algorithm is used to derive weights for selection of the most relevant past cases. In this setting, we examine the relative value of using input features of a problem for case selection versus using features inferred via KBR, versus both. We find that using both types of features is best (compared to human selection), but that input features are most helpful and inferred features are of marginal value. This finding supports the idea that KBR and CBR provide complimentary rather than redundant information, and hence that their combination in a single system is likely to be useful

Development of a Large-Scale Integrated Neurocognitive Architecture Part 1: Conceptual Framework

The idea of creating a general purpose machine intelligence that captures many of the features of human cognition goes back at least to the earliest days of artificial intelligence and neural computation. In spite of more than a half-century of research on this issue, there is currently no existing approach to machine intelligence that comes close to providing a powerful, general-purpose human-level intelligence. However, substantial progress made during recent years in neural computation, high performance computing, neuroscience and cognitive science suggests that a renewed effort to produce a general purpose and adaptive machine intelligence is timely, likely to yield qualitatively more powerful approaches to machine intelligence than those currently existing, and certain to lead to substantial progress in cognitive science, AI and neural computation. In this report, we outline a conceptual framework for the long-term development of a large-scale machine intelligence that is based on the modular organization, dynamics and plasticity of the human brain. Some basic design principles are presented along with a review of some of the relevant existing knowledge about the neurobiological basis of cognition. Three intermediate-scale prototypes for parts of a larger system are successfully implemented, providing support for the effectiveness of several of the principles in our framework. We conclude that a human-competitive neuromorphic system for machine intelligence is a viable long- term goal, but that for the short term, substantial integration with more standard symbolic methods as well as substantial research will be needed to make this goal achievable

NGS2 DARPA Program

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