Marker-Passing Inference in the Scone Knowledge-Base System

Fahlman, Scott E.

doi:10.1007/11811220_11

Cited by 27 publications

(32 citation statements)

References 9 publications

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“…However, it cannot determine if an email contains multiple tasks of the same type. To improve classification performance, Scone [7], which is RADAR's knowledge base, provides additional ontological information that is not contained in the email's content. Examples include basic facts, such as "the Connan room is in the University Center," and higher-level concepts, such as "a peanut is kind of food that people might be allergic to.…”

Section: The Action Listmentioning

confidence: 99%

“…RADAR was a large interdisciplinary project to build a suite of intelligent agents that help office workers complete tasks more efficiently. Over 100 faculty, staff, and students worked on RADAR components including the Email Classifier [25] and Multitask Coordination Assistant, along with other components including a Natural Language Processor [17], a Knowledge Base [7], a Schedule Optimizer [9], a Webmaster [27], a Briefing Assistant [15], and a Task Management Architecture [11]. Freed provides a more detailed description of the overall RADAR approach, architecture, and agents [10].…”

Section: Introductionmentioning

confidence: 99%

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Agent-assisted task management that reduces email overload

Faulring

Myers

Mohnkern

et al. 2010

Proceedings of the 15th International Conference on Intelligent User Interfaces

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RADAR is a multiagent system with a mixed-initiative user interface designed to help office workers cope with email overload. RADAR agents observe experts to learn models of their strategies and then use the models to assist other people who are working on similar tasks. The agents' assistance helps a person to transition from the normal emailcentric workflow to a more efficient task-centric workflow. The Email Classifier learns to identify tasks contained within emails and then inspects new emails for similar tasks. A novel task-management user interface displays the found tasks in a to-do list, which has integrated support for performing the tasks. The Multitask Coordination Assistant learns a model of the order in which experts perform tasks and then suggests a schedule to other people who are working on similar tasks. A novel Progress Bar displays the suggested schedule of incomplete tasks as well as the completed tasks. A large evaluation demonstrated that novice users confronted with an email overload test performed significantly better (a 37% better overall score with a factor of four fewer errors) when assisted by the RADAR agents.

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Section: The Action Listmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Agent-assisted task management that reduces email overload

Faulring

Myers

Mohnkern

et al. 2010

Proceedings of the 15th International Conference on Intelligent User Interfaces

View full text Add to dashboard Cite

show abstract

“…In this sense, the automation of 565 the reasoning task requires a language and a syntax, a knowledge base comprising the available information and rules, and a consistency checking mechanism that makes use of the available knowledge base and information provided by the sensors to infer new coherent information. Our current common-sense framework has been implemented using Scone (Fahlman, 2006) due to its suitability 570 for modelling actions and human behaviour. By using Scone, it is possible to encode, using a LISP-like syntax, formal definitions describing the World knowledge (WK) and Domain specific knowledge (DSK), as well as the expected set of behaviors, here referred as expectations (EXP).…”

Section: Sources Of Knowledgementioning

confidence: 99%

“…According to Woodaward (Woodward, 2003) (Fahlman, 2006) (Fahlman, 2011). It also provides an efficient mechanism, using an abstraction called context, for managing a priori inconsistent knowledge.…”

mentioning

confidence: 99%

Hierarchical Task Network planning with common-sense reasoning for multiple-people behaviour analysis

Santofimia

Rincón

Hong

et al. 2017

Expert Systems with Applications

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Safety on public transport is a major concern for the relevant authorities. We address this issue by proposing an automated surveillance platform which combines data from video, infrared and pressure sensors. Data homogenisation and integration is achieved by a distributed architecture based on communication middleware that resolves interconnection issues, thereby enabling data modelling. A common-sense knowledge base models and encodes knowledge about public-transport platforms and the actions and activities of passengers. Trajectory data from passengers is modelled as a time-series of human activities.Common-sense knowledge and rules are then applied to detect inconsistencies or errors in the data interpretation. Lastly, the rationality that characterises human behaviour is also captured here through a bottom-up Hierarchical Task Network planner that, along with common-sense, corrects misinterpretations to explain passenger behaviour. The system is validated using a simulated bus saloon scenario as a case-study. Eighteen video sequences were recorded with up to six passengers. Four metrics were used to evaluate performance. The system, with an accuracy greater than 90% for each of the four metrics, was found to outperform a rule-base system and a system containing planning alone.

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“…This theory has been successfully translated into a computational model by means of what S. Fahlman has come to call multiple-contexts [10].…”

Section: Knowledge Modelingmentioning

confidence: 99%

Common-Sense Knowledge for a Computer Vision System for Human Action Recognition

Santofimia

Rincón

Nebel

2012

Lecture Notes in Computer Science

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Abstract. This work presents a novel approach for human action recognition based on the combination of computer vision techniques and common-sense knowledge and reasoning capabilities. The emphasis of this work is on how common sense has to be leveraged to a vision-based human action recognition so that nonsensical errors can be amended at the understanding stage. The proposed framework is to be deployed in a realistic environment in which humans behave rationally, that is, motivated by an aim or a reason.

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Marker-Passing Inference in the Scone Knowledge-Base System

Cited by 27 publications

References 9 publications

Agent-assisted task management that reduces email overload

Agent-assisted task management that reduces email overload

Hierarchical Task Network planning with common-sense reasoning for multiple-people behaviour analysis

Common-Sense Knowledge for a Computer Vision System for Human Action Recognition

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