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Publisher Summary Most research on machine learning and planning has involved performance systems based on classical problem-solving algorithms (for example, STRIPS-Iike planners). AI problem solving has taken various divergent roads from these classical roots; two common current trends are reactive systems embedded in an environment and integrated multicomponent architectures. As performance engines, these advanced systems give rise to new learning problems—both in the sense of new opportunities and new difficulties. This chapter discusses new problems for machine learning. Classical problem-solving systems are typically consisted of a single component with a limited range of objectives and capabilities. Some current research efforts adopt a more holistic, synergistic approach involving integrated architectures with a broader scope of objectives and capabilities. These architectures integrate multiple performance components or multiple styles of reasoning. New issues arise within the context of integrated architectures, which engender new requirements and opportunities for machine learning. |
