Human cognition organizes knowledge in different complexity levels: higher-level knowledge is formed by first acquiring simple concepts, which are then combined to learn complex ones. As a result, many cognitive architectures use structural models to represent relations between knowledge of different complexity. Structural modeling has led to a number of representation and reasoning formalisms including frames, schemas, abstractions, hierarchical task networks (HTNs), and goal graphs among others. These formalisms have in common the use of certain kinds of constructs (e.g., objects, goals, skills, and tasks) that represent knowledge of varying degrees of complexity and that are connected through structural relations.

In recent years, we have observed increasing interest towards the problem of learning such structural knowledge from observations. These observations range from traces generated by an automated planner to video feeds from a robot performing some actions. Researchers have been addressing instances of this problem from different perspectives in a variety of research communities, among others including

• Machine Learning (including inductive logic programming (ILP))
• Automated Planning
• Case-Based Reasoning
• Cognitive Science

We believe that the time is ripe to get together researchers from these and other communities that are looking into instances of this problem and share ideas and perspectives in a common forum. Potential focus topics include but are not limited to:

• Cognitive architectures and learning techniques such as ILP, explanation-based learning (EBL), abstraction, generalization, and teleoreactive logic programs
• Formalisms for goal-directed behavior, including hierarchical task networks, skill hierarchies, goal networks, and annotated goal hierarchies
• Learning behavior from observations over time
• Observations ranging from fully to partially observable inputs and from annotated to un-annotated action traces
• Trade-offs between task performance and structural learning
• Learning meta-level knowledge (i.e., how to choose among different reasoning/problem-solving functionalities, how to manage the trade-offs between task performance and learning)
• Probabilistic and other extensions to structural knowledge to represent uncertainty
• Representing and learning continuous information
• Interacting with the external environment during structural learning (i.e., information-gathering, execution, etc)
• Learning structural information/data flow from observations

Workshop website: http://www.cs.umd.edu/~ukuter/struck09/ If you have any questions/comments and experience any problems with paper submissions, please let us know at struck09@easychair.org.

Activities performed organizing this workshop were in part supported by the National Science Foundation under Grant No. NSF 0642882.