Notice bibliographique
Résumé
Mental Model Ascription by Language-Enabled Intelligent Agents Marjorie McShane (marge@umbc.edu) Department of Computer Science and Electrical Engineering University of Maryland Baltimore County Baltimore, MD, 21250, USA The main goal of the workshop is to foster mutual learning, discussion and future collaboration among researchers pursuing agent-oriented mental model ascription in integrative cognitive architectures. Topic and Goal 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 computational modeling of mental model ascription offers an excellent opportunity to explore the interaction of traditionally separate modules of cognitive architectures, such as language understanding, plan- and goal-oriented reasoning, and memory management. The study of mental model ascription can benefit from advances in fields as disparate as machine reasoning, social interaction, developmental psychology, robotics, emotion, philosophy and computational linguistics, to name just a few. 1 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. Topics of interest include but are not limited to: Program Committee (confirmed) Ron Artstein (USC) Jerry Ball (Air Force Research Laboratory) Paul Bello (Office of Naval Research) Graeme Hirst (University of Toronto) Eva Hudlicka (Psychometrix Associates, Inc.) Pat Langley (University of Auckland, NZ and CMU) Marjorie McShane, Chair (UMBC) Sergei Nirenburg (UMBC) Massimo Poesio (University of Essex) Chris Potts (Stanford University) Yorick Wilks (IHMC). Organizational This will be a full day workshop that will include invited talks, talks selected by abstract submission, a round table discussion, and, optionally, a poster session. Talks will be grouped by similarity of theme and approach, and the schedule will allow for extended discussion of each group of presentations, best exploiting the workshop genre. We expect 30-40 participants that include students and researchers with broad interests in the computational modeling of cognition and/or psychologically-inspired natural language processing. The final session of the day will be devoted to planning a special journal issue (for Advances in Cognitive Systems) of papers inspired by the workshop. There are no special requirements for participants in the workshop. The workshop website is http://ilit.umbc.edu/Workshop/MentalModelCogSci2013.html . The contact email is mentalmodel2013@gmail.com. The workshop organizer, Marjorie McShane, has been working in the field of AI-NLP for the past fifteen years, with recent work focusing on the development of cognitive simulations of virtual patients to support clinician training. For a brief CV and list of publications, see http://ilit.umbc.edu/PubMcShane.htm. Developing computational treatments of language phenomena (e.g., indirect speech acts, irony, paraphrase, humor, coercion) that require or give rise to mental model ascription. Applying computational models of other cognitive capabilities (dialog, emotion, agent collaboration/competition and plan- and goal- oriented reasoning) to mental model ascription. 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. 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. Modeling dynamic belief modification, including overriding a previous belief and managing memories with respect to modified beliefs. As a comparison, CogSci 2012 featured a workshop, Modeling the Perception of Intention that treated intention recognition with an emphasis on visual perception.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,003 | 0,012 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,001 |
| Méta-épidémiologie (sens large) | 0,000 | 0,002 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,003 |
| Communication savante | 0,004 | 0,006 |
| Science ouverte | 0,001 | 0,004 |
| Intégrité de la recherche | 0,001 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».