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. In the realm of theory%2C we prove that the new constraints provide lower bounds on the cost of plans that comply with observations. We also provide an extensive empirical evaluation to assess how the new constraints improve the quality of the solution%2C and we found that they are especially informed in deciding which goals are unlikely to be part of the solution. Our novel recognition approaches have two pivotal advantages: first%2C they employ new IP%2FLP constraints for efficiently recognizing goals%3B second%2C we show how the new IP%2FLP constraints can improve the recognition of goals under both partial and noisy observability.?quality=80&w=800)
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