Goal Identification Control Using an Information Entropy-Based Goal Uncertainty Metric
Recent research has found situations where the identification of agent goals could be purposefully controlled, either by changing the underlying environment to make it easier, or exploiting it during agent planning to delay the opponent’s goal recognition. The paper tries to answer the fol...
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doaj-311316d57f5148ca8aceb1a0b55fe3722020-11-24T21:44:22ZengMDPI AGEntropy1099-43002019-03-0121329910.3390/e21030299e21030299Goal Identification Control Using an Information Entropy-Based Goal Uncertainty MetricKai Xu0Quanjun Yin1College of System Engineering, National University of Defense Technology, Changsha 410000, ChinaCollege of System Engineering, National University of Defense Technology, Changsha 410000, ChinaRecent research has found situations where the identification of agent goals could be purposefully controlled, either by changing the underlying environment to make it easier, or exploiting it during agent planning to delay the opponent’s goal recognition. The paper tries to answer the following questions: what kinds of actions contain less information and more uncertainty about the agent’s real goal, and how to describe this uncertainty; what is the best way to control the process of goal identification. Our contribution is the introduction of a new measure we call <i>relative goal uncertainty (rgu)</i> with which we assess the goal-related information that each action contains. The <i>rgu</i> is a relative value associated with each action and represents the goal uncertainty quantified by information entropy after the action is taken compared to other executable ones in each state. After that, we show how goal vagueness could be controlled either for one side or for both confronting sides, and formulate this goal identification control problem as a mixed-integer programming problem. Empirical evaluation shows the effectiveness of the proposed solution in controlling goal identification process.https://www.mdpi.com/1099-4300/21/3/299goal uncertaintygoal recognitiongoal identification controlinformation entropy |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Kai Xu Quanjun Yin |
spellingShingle |
Kai Xu Quanjun Yin Goal Identification Control Using an Information Entropy-Based Goal Uncertainty Metric Entropy goal uncertainty goal recognition goal identification control information entropy |
author_facet |
Kai Xu Quanjun Yin |
author_sort |
Kai Xu |
title |
Goal Identification Control Using an Information Entropy-Based Goal Uncertainty Metric |
title_short |
Goal Identification Control Using an Information Entropy-Based Goal Uncertainty Metric |
title_full |
Goal Identification Control Using an Information Entropy-Based Goal Uncertainty Metric |
title_fullStr |
Goal Identification Control Using an Information Entropy-Based Goal Uncertainty Metric |
title_full_unstemmed |
Goal Identification Control Using an Information Entropy-Based Goal Uncertainty Metric |
title_sort |
goal identification control using an information entropy-based goal uncertainty metric |
publisher |
MDPI AG |
series |
Entropy |
issn |
1099-4300 |
publishDate |
2019-03-01 |
description |
Recent research has found situations where the identification of agent goals could be purposefully controlled, either by changing the underlying environment to make it easier, or exploiting it during agent planning to delay the opponent’s goal recognition. The paper tries to answer the following questions: what kinds of actions contain less information and more uncertainty about the agent’s real goal, and how to describe this uncertainty; what is the best way to control the process of goal identification. Our contribution is the introduction of a new measure we call <i>relative goal uncertainty (rgu)</i> with which we assess the goal-related information that each action contains. The <i>rgu</i> is a relative value associated with each action and represents the goal uncertainty quantified by information entropy after the action is taken compared to other executable ones in each state. After that, we show how goal vagueness could be controlled either for one side or for both confronting sides, and formulate this goal identification control problem as a mixed-integer programming problem. Empirical evaluation shows the effectiveness of the proposed solution in controlling goal identification process. |
topic |
goal uncertainty goal recognition goal identification control information entropy |
url |
https://www.mdpi.com/1099-4300/21/3/299 |
work_keys_str_mv |
AT kaixu goalidentificationcontrolusinganinformationentropybasedgoaluncertaintymetric AT quanjunyin goalidentificationcontrolusinganinformationentropybasedgoaluncertaintymetric |
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1725910881540243456 |