The Design & Intelligence Laboratory (DIL) conducts research on design cognition and design AI. Our work focuses on knowledge-based techniques of reasoning and learning, including functional modeling, analogical reasoning, visual reasoning, and meta-reasoning. Creative design of physical systems (e.g., biologically inspired design), design of self-adaptive software agents (e.g., self-adaptive game-playing agents), and visual cognition (e.g., visual analogy) form the context for much of this research.
The long-term, big-picture questions that drive our research are: (1) What is creativity in design, how may we build computer systems that can generate creative designs, and how might we build interactive environments to aid human creativity? (2) What is reflection in intelligent agents, how may we design software agents that can reflect on their knowledge, reasoning, and behavior for self-explanation and self-adaptation? (3) What is visual analogy in human cognition, and how may we build interactive environments and tools for design that use the human capacity for visual reasoning?
From the viewpoint of design, DIL develops theories, techniques and tools that enable innovative design of physical systems and design of self-adaptive software agents. The goals of the research on conceptual design of physical systems (e.g., biologically inspired engineering design) is to develop theories of creativity in design and to build interactive tools for aiding innovative design. The goals of the research on design of self-adaptive software agents (e.g., game-playing agents) is to develop theories of reflection in intelligent agents, and to build interactive tools for supporting adaptations to designs of software agents.
From the perspective of artificial intelligence, we develop theories and techniques of functional modeling, and analogical, visual and meta-reasoning that provide unified accounts of memory, reasoning and learning, and integrated accounts of the content/representation of knowledge and the processes of reasoning/learning. Results of this research are in the form of architectures, processes, methods and algorithms for reasoning and learning, and corresponding languages for knowledge representation and organization. Products also include knowledge-based systems and tools that embody the methods of reasoning and learning.
From the viewpoint of cognitive science, we develops theories of visual and multi-modal reasoning in cognition, including atypical cognition such as cognition in autism, and their implications for design of interactive environments and tools.
The Design & Intelligence Laboratory is affiiliated with the
AI & CogSci Laboratory, the Cognitive Science
Group, the
Graphics, Visualization and Usability Center, and the
Center for Biologically Inspired Design.
Research Grants
Over the years, DIL's research has been generously supported by
the National Science Foundation,
the Defense Advanced Research Projects Agency,
the Office of Naval Research,
the Department of Homeland Security
through the Pacific Northwest National Laboratory, and
Northern Telecom, NCR , and NEC.
Current grants:
Multi-Modal Case-Based Reasoning in Modeling and Design. PI: Ashok Goel. NSF IIS
Grant.
Visual Analytics. PI: William Ribarsky (UNCC);
Georgia Tech PI: John Stasko, Georgia Tech Co-PIs: James Foley, Ashok Goel, Rebecca Grinter. DHS Grant under the auspices of NVAC (PNNL) and the Southeastern
Regional Visual Analytics Center .
Teleological Reasoning in Adaptive Software Design. PI: Ashok Goel; Co-PI: Spencer Rugaber.
NSF SoD Grant.
Learning about Complex Systems by Constructing Structure-Behavior-Function Models in Middle Schools. PI: Ashok Goel. Co-PI: Spencer Rugaber,
Cindy Hmelo-Silver (Rutgers), and Rebecca Jordan (Rutgers). NSF ALT Grant.
Towards a Computational Model of Biological Analogies in Innovative
Engineering Design. PI: Ashok Goel. NSF CreativeIT SGER Grant.
Recent Grants
Learning about Causal Models of Complex Systems. PI: N. Hari Narayanan (Auburn); Co-PIs: Ashok Goel, Cindy Hmelo-Silver (Rutgers), Teresa Hubscher-Young (RPI) & Sadhana Puntambekar (Wisconsin). NSF SLC Catalyst Grant.