1. Companion Cognitive Systems: A Step toward Human-Level AI
Kenneth D. Forbus and Thomas R. Hinrichs
AI Magazine 2006

2. Companion Cognitive Systems Our Approach
Robust Reasoning and Learning
Longevity and Performance
Interactivity
Modeling in Companions
Architecture
Experiment
Relate / Future Work

3. Companion Cognitive Systems
Companions will be software aide-de-camps, collaborators with their users.
Robust reasoning and learning.
Longevity.
Interactivity.

4. Our Approach Robust Reasoning and Learning Longevity and Performance
analogical reasoning and learning from experience.
analogical processing : SME , MAC/FAC
Longevity and Performance
a distributed agent architecture, hosted on cluster computers
“hot-swappable”
Interactivity
sketch understanding
Relational concept maps

5. Modeling in Companions
Situation and Domain Models.
capture the current problem and relevant knowledge about it.
Task and Dialogue Models.
describe the shared task and where the human/computer partnership is in working on it.
User Models.
capture the idiosyncratic preferences, habits, and utilities of the human partner(s).
Self Models.
provide the Companion’s own understanding of its operations, abilities, and preferences

6. First-Cut Companion Architecture

7. Experimental Domain : Everyday Physical Reasoning (1)
68 Bennett Mechanical Comprehension test
Which wheelbarrow would be easier to lift?

8. Everyday Physical Reasoning (2)
sketching Knowledge Entry Associate (sKEA) : interface.
Visual/conceptual mappings and conventions for depicting everyday objects,
modeling assumptions
causal models

9. Everyday Physical Reasoning (3)
focused on learning visual/conceptual mappings
the wheel/axle relationship in a wheelbarrow being a rotational connection.
explored whether accumulating examples of physical principles could enable a system to solve Bennett Mechanical Comprehension test problems.

10. Everyday Physical Reasoning (4)

11. Everyday Physical Reasoning (5)

12. Everyday Physical Reasoning (6)

13. Relate Work skill learning. Conceptual understanding and learning.
analogical processing,
distributed agent architecture
large-scale experiments on existing hardware,
Representation construction

14. Future Work A script-based Interaction manager SEQL agent HTN planner
Incorporates probabilities in its generalizations.
HTN planner
to handle strategies and tactics in FreeCiv, support plan recognition, and run the Executive.
Decomposing our sketching software.
variety of interesting questions
Self-awareness
Encoding
Nonlinguistic multimodal communication.