Acquiring planning domain models using <i>LOCM</i>

Cresswell, Stephen; McCluskey, T.L.; West, Margaret M.

doi:10.1017/s0269888912000422

Cited by 84 publications

(68 citation statements)

References 7 publications

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“…Ever since then, there have been many researches which have attempted to convert planning problem into model detection problem and linear programming problem. The corresponding planning systems Alt [2], MIPS [3], SGPlan [13] have displayed good performances.…”

Section: Convergence Transformation Planning Methods and Fusion Self-amentioning

confidence: 99%

Intelligent Decision Support Algorithm Based on Self-Adaption Reasoning

Chen¹,

Jin²,

Wang³

et al. 2017

INT J COMPUT COMMUN

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This paper analyzes the logic deduction and reasoning techniques used in several intelligent decision support algorithms, and proposes a flexible planning method GARIv using fuzzy descriptive logic in media enterprise management. Combined with experiments, the above methods are illustrated in terms of effectiveness and feasibility. In the end, the challenges and possible solutions of intelligent decision support algorithms with self-adaption reasoning are discussed.

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Section: Convergence Transformation Planning Methods and Fusion Self-amentioning

confidence: 99%

Intelligent Decision Support Algorithm Based on Self-Adaption Reasoning

Chen¹,

Jin²,

Wang³

et al. 2017

INT J COMPUT COMMUN

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“…One significant challenge faced in autonomous knowledge acquisition is that of learning from incomplete and noisy data sources. Researchers have produced tools such as Opmaker (McCluskey et al, 2009), Learning object-centric models or LOCM (Cresswell et al, 2013) and Action-Model Acquisition from Noisy plan traces or AMAN (Zhuo and Kambhampati, 2013). The learning process of these tools is dynamic and sometimes take additional information (such as partial domain model and uncertainty factors) to output a full domain model.…”

Section: Related Workmentioning

confidence: 99%

Eliciting and utilising knowledge for security event log analysis: An association rule mining and automated planning approach

Khan

Parkinson

2018

Expert Systems with Applications

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Vulnerability assessment and security configuration activities are heavily reliant on expert knowledge. This requirement often results in many systems being left insecure due to a lack of analysis expertise and access to specialist resources. It has long been known that a system's event logs provide historical information depicting potential security breaches, as well as recording configuration activities. However, identifying and utilising knowledge within the event logs is challenging for the non-expert. In this paper, a novel technique is developed to process security event logs of a computer that has been assessed and configured by a security professional, extract key domain knowledge indicative of their expert decision making, and automatically apply learnt knowledge to previously unseen systems by non-experts.The technique converts event log entries into an object-based model and dynamically extracts associative rules. The rules are further improved in terms of quality using a temporal metric to autonomously establish temporal-association rules and acquire a domain model of expert configuration tasks. The acquired domain model and problem instance generated from a previously unseen system can then be used to produce a plan-of-action, which can be exploited by non-professionals to improve their system's security. Empirical analysis is subsequently performed on 20 event logs, where identified plan traces are discussed in terms of accuracy and performance.

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“…As a variation on this, domain models can be formulated by automated acquisition tools, such as in the LAMP system [42], and the LOCM system [6]. These type of systems acquire domain models from example plans with little or no pre-engineered domain knowledge.…”

Section: Model Qualitymentioning

confidence: 99%

Engineering Knowledge for Automated Planning

McCluskey

Vaquero

Vallati

2017

Proceedings of the Knowledge Capture Conference

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Automated planning is a prominent Artificial Intelligence challenge, as well as being a common capability requirement for intelligent autonomous agents. A critical aspect of what is called domainindependent planning, is the application knowledge that must be added to the planner to create a complete planning application. This is made explicit in (i) a domain model, which is a formal representation of the persistent domain knowledge, and (ii) an associated problem instance, containing the details of the particular problem to be solved. Both these components are used by automated planning engines for reasoning, in order to synthesize a solution plan. Formulating knowledge for use in planning engines is currently something of an ad-hoc process, where the skills of knowledge engineers significantly influence the quality of the resulting planning application. On top of that, a notion of quality of the knowledge captured within a domain model is missing; it is therefore hard to provide useful guidelines to knowledge engineers. This paper raises some issues relating to the engineering of application knowledge for automated planning, focussing on the domain model. It uses the idea of a domain model as a formal specification of a domain, and considers what it means to measure the quality of such a specification. To do this it proposes definitions of the attributes of a domain model and its encoding language, which are needed by the automated planning community in order to improve tools for supporting the engineering of planning knowledge, and to advance toward a shared and inclusive definition of quality of domain models.

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Acquiring planning domain models using LOCM

Cited by 84 publications

References 7 publications

Intelligent Decision Support Algorithm Based on Self-Adaption Reasoning

Intelligent Decision Support Algorithm Based on Self-Adaption Reasoning

Eliciting and utilising knowledge for security event log analysis: An association rule mining and automated planning approach

Engineering Knowledge for Automated Planning

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