Constructing generative logical models for optimisation problems using domain knowledge
In this paper we seek to identify data instances with a low value of some objective (or cost) function. Normally posed as optimisation problems, our interest is in problems that have the following characteristics: (a) optimal, or even near-optimal solutions are very rare; (b) it is expensive to obta...
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| Vydáno v: | Machine learning Ročník 109; číslo 7; s. 1371 - 1392 |
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| Médium: | Journal Article |
| Jazyk: | angličtina |
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01.07.2020
Springer Nature B.V |
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| ISSN: | 0885-6125, 1573-0565 |
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| Abstract | In this paper we seek to identify data instances with a low value of some objective (or cost) function. Normally posed as optimisation problems, our interest is in problems that have the following characteristics: (a) optimal, or even near-optimal solutions are very rare; (b) it is expensive to obtain the value of the objective function for large numbers of data instances; and (c) there is domain knowledge in the form of experience, rules-of-thumb, constraints and the like, which is difficult to translate into the usual constraints for numerical optimisation procedures. Here we investigate the use of Inductive Logic Programming (ILP) to construct models within a procedure that progressively attempts to increase the number of near-optimal solutions. Using ILP in this manner requires a change in focus from discriminatory models (the usual staple for ILP) to generative models. Using controlled datasets, we investigate the use of probability-sampling of solutions based on the estimated cost of clauses found using ILP. Specifically, we compare the results obtained against: (a) simple random sampling; and (b) generative deep network models that use a low-level encoding and automatically construct higher-level features. Our results suggest: (1) Against each of the alternatives, probability-sampling from ILP-constructed models contain more near-optimal solutions; (2) The key to the effectiveness of ILP-constructed models is the availability of domain knowledge. We also demonstrate the use of ILP in this manner on two real-world problems from the area of drug-design (predicting solubility and binding affinity), using domain knowledge of chemical ring structures and functional groups. Taken together, our results suggest that generative modelling using ILP can be very effective for optimisation problems where: (a) the number of training instances to be used is restricted, and (b) there is domain knowledge relevant to low-cost solutions. |
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| AbstractList | In this paper we seek to identify data instances with a low value of some objective (or cost) function. Normally posed as optimisation problems, our interest is in problems that have the following characteristics: (a) optimal, or even near-optimal solutions are very rare; (b) it is expensive to obtain the value of the objective function for large numbers of data instances; and (c) there is domain knowledge in the form of experience, rules-of-thumb, constraints and the like, which is difficult to translate into the usual constraints for numerical optimisation procedures. Here we investigate the use of Inductive Logic Programming (ILP) to construct models within a procedure that progressively attempts to increase the number of near-optimal solutions. Using ILP in this manner requires a change in focus from discriminatory models (the usual staple for ILP) to generative models. Using controlled datasets, we investigate the use of probability-sampling of solutions based on the estimated cost of clauses found using ILP. Specifically, we compare the results obtained against: (a) simple random sampling; and (b) generative deep network models that use a low-level encoding and automatically construct higher-level features. Our results suggest: (1) Against each of the alternatives, probability-sampling from ILP-constructed models contain more near-optimal solutions; (2) The key to the effectiveness of ILP-constructed models is the availability of domain knowledge. We also demonstrate the use of ILP in this manner on two real-world problems from the area of drug-design (predicting solubility and binding affinity), using domain knowledge of chemical ring structures and functional groups. Taken together, our results suggest that generative modelling using ILP can be very effective for optimisation problems where: (a) the number of training instances to be used is restricted, and (b) there is domain knowledge relevant to low-cost solutions. |
| Author | Vig, Lovekesh Srinivasan, Ashwin Shroff, Gautam |
| Author_xml | – sequence: 1 givenname: Ashwin orcidid: 0000-0002-4911-0038 surname: Srinivasan fullname: Srinivasan, Ashwin email: ashwin@goa.bits-pilani.ac.in organization: Department of Computer Science and Information Systems, BITS-Pilani – sequence: 2 givenname: Lovekesh surname: Vig fullname: Vig, Lovekesh organization: TCS Research – sequence: 3 givenname: Gautam surname: Shroff fullname: Shroff, Gautam organization: TCS Research |
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| Cites_doi | 10.1007/BF03037232 10.1007/s10994-009-5114-x 10.1007/s10994-015-5494-z 10.1023/A:1013500812258 10.1021/ci034184n 10.1016/j.sorms.2012.08.002 10.1016/0743-1066(94)90035-3 10.1007/s10472-009-9133-x 10.1016/j.ejor.2010.11.008 10.1016/j.ejor.2006.10.034 10.1023/A:1010980106294 10.1007/978-3-642-83189-8 10.1038/nature09107 10.1016/B978-1-55860-247-2.50048-6 10.1007/978-3-319-58694-6_25 10.1007/978-3-642-33275-3_2 10.1007/978-3-540-85928-4_14 10.1016/B978-1-55860-200-7.50078-7 10.1007/3540635149_56 10.1007/978-3-319-99960-9_2 |
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| DOI | 10.1007/s10994-019-05842-x |
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| Keywords | Inductive logic programming Generative models Domain knowledge guided optimisation |
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| SubjectTerms | Artificial Intelligence Computer Science Control Domains Functional groups Logic programming Machine Learning Mechatronics Natural Language Processing (NLP) Optimization Random sampling Ring structures Robotics Simulation and Modeling Special Issue of the Inductive Logic Programming (ILP) 2019 |
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