The Association for the Advancement of Artificial Intelligence, in cooperation with Stanford University's Department of Computer Science, presented the 2001 Spring Symposium Series on Monday through Wednesday, 26 to 28 March 2001, at Stanford University. The titles of the seven symposia were (1) Answer Set Programming: Toward Efficient and Scalable Knowledge, Representation and Reasoning, (2) Artificial Intelligence and Interactive Entertainment, (3) Game-Theoretic and Decision-Theoretic Agents, (4) Learning Grounded Representations, (5) Model-Based Validation of Intelligence, (6) Robotics and Education, and (7) Robust Autonomy.

The Association for the Advancement of Artificial Intelligence, in cooperation with Stanford University's Department of Computer Science, presented the 2001 Spring Symposium Series on Monday through Wednesday, 26 to 28 March 2001, at Stanford University. The titles of the seven symposia were (1) Answer Set Programming: Toward Efficient and Scalable Knowledge, Representation and Reasoning, (2) Artificial Intelligence and Interactive Entertainment, (3) Game-Theoretic and Decision-Theoretic Agents, (4) Learning Grounded Representations, (5) Model-Based Validation of Intelligence, (6) Robotics and Education, and (7) Robust Autonomy.

As a field, knowledge representation has often been accused of being off in a theoretical no-man's land, removed from, and largely unrelated to, the central issues in AI. This article argues that recent trends in KR instead demonstrate the benefits of the interplay between science and engineering, a lesson from which all AI could benefit. This article grew out of a survey talk on the Third International Conference on Knowledge Representation and Reasoning (KR-92) (Nebel, Rich, and Swartout 1992) that I presented at the Thirteenth International Joint Conference on Artificial Intelligence (IJCAI-93).

As a field, knowledge representation has often been accused of being off in a theoretical no-man's land, removed from, and largely unrelated to, the central issues in AI. This article argues that recent trends in KR instead demonstrate the benefits of the interplay between science and engineering, a lesson from which all AI could benefit. This article grew out of a survey talk on the Third International Conference on Knowledge Representation and Reasoning (KR-92) (Nebel, Rich, and Swartout 1992) that I presented at the Thirteenth International Joint Conference on Artificial Intelligence (IJCAI-93).