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This paper describes SiN, a novel case-based planning algorithm that combines conversational case retrieval with generative planning. SiN is provably correct, and can generate plans given an incomplete domain theory by using cases to extend that domain theory. SiN can also reason with imperfect world-state information by incorporing preferences into the(More)
SHOP and SHOP2 are HTN planning systems that were designed with two goals in mind: to investigate some research issues in automated planning, and to provide some simple, practical planning tools. They are available as freeware, and have developed an active base of users in government laboratories, industrial R&D projects, and academic settings. This paper(More)
Previous algorithms for learning lexicographic preference models (LPMs) produce a "best guess" LPM that is consistent with the observations. Our approach is more democratic: we do not commit to a single LPM. Instead, we approximate the target using the votes of a <i>collection</i> of consistent LPMs. We present two variations of this method---<i>variable(More)
Raising the level of abstraction for synthetic biology design requires solving several challenging problems, including mapping abstract designs to DNA sequences. In this paper we present the first formalism and algorithms to address this problem. The key steps of this transformation are feature matching, signal matching, and part matching. Feature matching(More)
Despite the fact that thousands of applications manipulate plans, there has been no work to date on managing large databases of plans. In this paper, we first propose a formal model of plan databases. We describe important notions of consistency and coherence for such databases. We then propose a set of operators similar to the relational algebra to query(More)
A long-standing goal of synthetic biology is to rapidly engineer new regulatory circuits from simpler devices. As circuit complexity grows, it becomes increasingly important to guide design with quantitative models, but previous efforts have been hindered by lack of predictive accuracy. To address this, we developed Empirical Quantitative Incremental(More)