To provide Intelligent Systems research and development services to help our customers develop innovative products. Intelligent Systems automate knowledge-intensive tasks, learn from their mistakes, and adapt to unexpected situations, offering capabilities beyond conventional systems.
Our members hold advanced degrees in computer science, engineering, neuroscience and business, and have many years of experience in information technology research and product development.
We have pioneered advancements in many AI topics such as goal-driven autonomy, relational learning, learning agents in virtual environments, information and knowledge extraction, information brokering, and automated video surveillance.
Floyd, M.W., Drinkwater, M., and Aha, D.W. (2016). Learning Trustworthy Behaviors Using an Inverse Trust Metric. In Mittu, R., Sofge, D., Wagner, A., and Lawless, W.F. (Eds.) Robust Intelligence and Trust in Autonomous Systems. Springer.
Alford, R., Shivashankar, V., Roberts, M., and Aha, D.W. (2016). Hierarchical Planning: Relating Task and Goal Decomposition with Task Sharing. In the 25th International Joint Conference on Artificial Intelligence (IJCAI 2016), New York, NY.
To, S.T., Roberts, M., Apker, T., Johnson, B., & Aha, D.W. (2016). Mixed propositional metric temporal logic: A new formalism for temporal planning. To appear in D. Magazzeni, S. Sanner, & S. Thiebaux (Eds.) Planning for Hybrid Systems: Papers from the AAAI Workshop (Technical Report WS-16-13). Phoenix, AZ: AAAI Press.
Turner, J., Gupta, K.M., Morris, B., Aha, D. (2016). Keypoint Density-based Region Proposal for Fine-Grained Object Detection and Classification using Regions with Convolutional Neural Network Features.
Aha, D.W., and Floyd, M.W. (2015). Preface - Workshop on Case-Based Agents. In Proceedings of the 23rd International Conference on Case-Based Reasoning Workshops, Frankfurt am Main, Germany, September 28 - 30, 9-11.
Floyd, M.W., Drinkwater, M., and Aha, D.W. (2015). Improving Trust-Guided Behavior Adaptation Using Operator Feedback. In Proceedings of the 23rd International Conference on Case-Based Reasoning, Frankfurt am Main, Germany, September 28 - 30, 134-148. Springer.
We conduct basic and applied research on a variety of AI topics such as goal-driven autonomy, natural language understanding, relational learning, learning and acting in virtual environments and automated web services brokering. We work with you to disseminate research findings via scientific publications and presentations.
We develop prototypes to showcase and evaluate AI R&D products. We employ software engineering best practices to architect stand-alone and embedded cross-platform systems that can be easily transitioned. We integrate products from multiple performer teams of a research program for demonstration and evaluation.
We develop test-beds for evaluating AI R&D products. Our test-beds provide easy access to extensible components; test problem sets, plug-and-play interfaces for target algorithms, and evaluation metrics. These enable bench-marking and comparison of a family of AI algorithms and technologies.
We test and evaluate AI methods and systems at individual performer and research program levels. We design evaluation methods and experiments, collect and prepare test data, develop evaluation software, conduct experiments, analyze observations, and report findings.
We provide the information technology infrastructure for research and collaboration across teams in a research program. For example, we can provide a Virtual Private Network (VPN) for conducting research with sensitive data or foster collaboration with a program-wide Wiki.
We develop program plans with “byte-sized” tasks and realistic milestones. We monitor project execution and deliver high-quality research products and solutions that exceed your customer’s expectations.