24.1617, Calls: Cognitive Science, Computational Linguistics/Germany

Tue Apr 9 18:28:17 UTC 2013

LINGUIST List: Vol-24-1617. Tue Apr 09 2013. ISSN: 1069 - 4875.

Subject: 24.1617, Calls: Cognitive Science, Computational Linguistics/Germany

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Date: Tue, 09 Apr 2013 14:27:39
From: Marjorie McShane [mentalmodel2013 at gmail.com]
Subject: Workshop: Mental Model Ascription by Language-Enabled Intelligent Agents

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Full Title: Workshop: Mental Model Ascription by Language-Enabled Intelligent Agents 

Date: 31-Jul-2013 - 31-Jul-2013
Location: Berlin, Germany 
Contact Person: Marge McShane
Meeting Email: mentalmodel2013 at gmail.com
Web Site: http://ilit.umbc.edu/Workshop/MentalModelCogSci2013.html 

Linguistic Field(s): Cognitive Science; Computational Linguistics 

Call Deadline: 15-May-2013 

Meeting Description:

This will be a full-day workshop at the 35th Annual Meeting of the Cognitive Science Society.

Mental model ascription can be defined as inferring features of another human or artificial agent that cannot be directly observed, such as that agent’s beliefs, plans, goals, intentions, personality traits, mental and emotional states, and knowledge about the world. This capability is an essential functionality of intelligent agents if they are to engage in sophisticated collaborations with people. The common thread of this workshop will be the computational modeling of unobservable features by intelligent agents using language input as at least one of their modes of perception.

Call for Papers:

Paper presentation will be selected based on abstracts of not more than one page. Topics of interest include but are not limited to: 

1. Developing computational treatments of language phenomena (e.g., indirect speech acts, irony, paraphrase, humor, coercion) that require or give rise to mental model ascription.
2. Applying computational models of other cognitive capabilities (dialog, emotion, agent collaboration/competition and plan- and goal-oriented reasoning) to mental model ascription.
3. Modeling agent decisions about what to learn about other agents’ unobservable features, considering that attempting to learn everything in every context would incur a heavy cognitive load.
4. Modeling how agents measure their confidence in the results of mental model ascription, which will be affected by their confidence in their understanding of contributing linguistic (or other) percepts as well as their ability to make valid inferences.
5. Modeling dynamic belief modification, including overriding a previous belief and managing memories with respect to modified beliefs.

We particularly encourage abstracts about topics that will lead to discussion, which will best exploit the workshop format.

Abstracts should be submitted as pdf files to mentalmodel2013 at gmail.com.

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