Learning revised models for planning in adaptive systems

Environment domain models are a key part of the information used by adaptive systems to determine their behaviour. These models can be incomplete or inaccurate. In addition, since adaptive systems generally operate in environments which are subject to change, these models are often also out of date....

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Published in:2013 35th International Conference on Software Engineering (ICSE) pp. 63 - 71
Main Authors: Sykes, Daniel, Corapi, Domenico, Magee, Jeff, Kramer, Jeff, Russo, Alessandra, Inoue, Katsumi
Format: Conference Proceeding
Language:English
Published: IEEE 01.05.2013
Subjects:
Adaptation models
Adaptive systems
Computational modeling
feedback
machine learning
Planning
Probabilistic logic
Robot sensing systems
runtime model
software architecture
ISBN:9781467330732, 1467330736
ISSN:0270-5257
Online Access:Get full text
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Abstract Environment domain models are a key part of the information used by adaptive systems to determine their behaviour. These models can be incomplete or inaccurate. In addition, since adaptive systems generally operate in environments which are subject to change, these models are often also out of date. To update and correct these models, the system should observe how the environment responds to its actions, and compare these responses to those predicted by the model. In this paper, we use a probabilistic rule learning approach, NoMPRoL, to update models using feedback from the running system in the form of execution traces. NoMPRoL is a technique for nonmonotonic probabilistic rule learning based on a transformation of an inductive logic programming task into an equivalent abductive one. In essence, it exploits consistent observations by finding general rules which explain observations in terms of the conditions under which they occur. The updated models are then used to generate new behaviour with a greater chance of success in the actual environment encountered.
AbstractList Environment domain models are a key part of the information used by adaptive systems to determine their behaviour. These models can be incomplete or inaccurate. In addition, since adaptive systems generally operate in environments which are subject to change, these models are often also out of date. To update and correct these models, the system should observe how the environment responds to its actions, and compare these responses to those predicted by the model. In this paper, we use a probabilistic rule learning approach, NoMPRoL, to update models using feedback from the running system in the form of execution traces. NoMPRoL is a technique for nonmonotonic probabilistic rule learning based on a transformation of an inductive logic programming task into an equivalent abductive one. In essence, it exploits consistent observations by finding general rules which explain observations in terms of the conditions under which they occur. The updated models are then used to generate new behaviour with a greater chance of success in the actual environment encountered.
Author Russo, Alessandra
Corapi, Domenico
Sykes, Daniel
Inoue, Katsumi
Kramer, Jeff
Magee, Jeff
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  givenname: Alessandra
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  givenname: Katsumi
  surname: Inoue
  fullname: Inoue, Katsumi
  email: inoue@nii.ac.jp
  organization: Nat. Inst. of Inf., Tokyo, Japan
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Snippet Environment domain models are a key part of the information used by adaptive systems to determine their behaviour. These models can be incomplete or...
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StartPage 63
SubjectTerms Adaptation models
Adaptive systems
Computational modeling
feedback
machine learning
Planning
Probabilistic logic
Robot sensing systems
runtime model
software architecture
Title Learning revised models for planning in adaptive systems
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