Reports of the AAAI 2008 Spring Symposia
Bibliographic record
Abstract
The Association for the Advancement of Artificial Intelligence (AAAI) was pleased to present the AAAI 2008 Spring Symposium Series, held Wednesday through Friday, March 26‐28, 2008, at Stanford University, California. The eight symposia were titled (1) AI Meets Business Rules and Process Management, (2) Architectures for Intelligent Theory‐Based Agents, (3) Creative Intelligent Systems, (4) Emotion, Personality, and Social Behavior, (5) Semantic Scientific Knowledge Integration, (6) Social Information Processing, (7) Symbiotic Relationships between Semantic Web and Knowledge Engineering, (8) Using AI to Motivate Greater Participation in Computer Science. The goal of the AI Meets Business Rules and Process Management AAAI symposium was to investigate the various approaches and standards to represent business rules, business process management, and the semantic web with respect to expressiveness and reasoning capabilities. The focus of the Architectures for Intelligent Theory‐Based Agents AAAI symposium was the definition of architectures for intelligent theory‐based agents, comprising languages, knowledge representation methodologies, reasoning algorithms, and control loops. The Creative Intelligent Systems symposium included five major discussion sessions and a general poster session (in which all contributing papers were presented). The purpose of this symposium was to explore the synergies between creative cognition and intelligent systems. The goal of the Emotion, Personality, and Social Behavior symposium was to examine fundamental issues in affect and personality in both biological and artificial agents, focusing on the roles of these factors in mediating social behavior. The Semantic Scientific Knowledge Integration symposium brought together the semantic technologies community with the scientific information technology community in an effort to build the general semantic science information community. The Social Information Processing symposium's goal was to investigate computational and analytic approaches that will enable users to harness the efforts of large numbers of other users to solve a variety of information processing problems, from discovering high‐quality content to managing common resources. The goal of the Symbiotic Relationships between the Semantic Web and Software Engineering symposium was to explore how the lessons learned by the knowledge‐engineering community over the past three decades could be applied to the bold research agenda of current workers in semantic web technologies. The purpose of the Using AI to Motivate Greater Participation in Computer Science symposium was to identify ways that topics in AI may be used to motivate greater student participation in computer science by highlighting fun, engaging, and intellectually challenging developments in the AI‐related curriculum at a number of educational levels. Technical reports of the symposia were published by AAAI Press.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.176 | 0.117 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".