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.
Shivashankar, V., Alford, R., Roberts, M., and Aha, D.W. (2016). Cost-Optimal Algorithms for Planning with Procedural Control Knowledge. In Proceedings of the 22nd European Conference on Artificial Intelligence (ECAI 2016). To Appear.
Roberts, M., Alford, R., Shivashankar, V., Leece, M., Gupta, S., and Aha, D.W. (2016). Goal Reasoning, Planning, and Acting with ActorSim, The Actor Simulator. In Proceedings of the Fourth Annual Conference on Advances in Cognitive Systems (ACS 2016), Evanston IL, 2016.
Roberts, M., Alford, R., Shivashankar, V., Leece, M., Gupta, S., and Aha, D.W. (2016). Goal Reasoning, Planning, and Acting with ActorSim, The Actor Simulator. In ICAPS Workshop on Planning and Robotics (PlanRob 2016), London, 2016.
Shivashankar, V., Alford, R., Roberts, M., and Aha, D.W. (2016). Cost-Optimal Algorithms for Hierarchical Goal Network Planning: A Preliminary Report. In the ICAPS Workshop on Heuristics and Search for Domain-independent Planning (HSDIP 2016), London, 2016.
Gillespie, K., Floyd, M.W., Molineaux, M., Vattam, S.S., and Aha, D.W. (2016). Semantic Classification of Utterances in a Language-driven Game. In Proceedings of the Computer Games Workshop (held at the 25th International Joint Conference on Artificial Intelligence), New York, New York, USA, July 9 - 15, to appear.
Karneeb, J., Floyd, M.W., Moore, P., and Aha, D.W. (2016). Distributed Discrepancy Detection for BVR Air Combat. In Proceedings of the Workshop on Goal Reasoning (held at the 25th International Joint Conference on Artificial Intelligence), New York, New York, USA, July 9 - 15, to appear.
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 Proceedings of 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.
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.