Skip to main content
Department of Computer Science

Artificial Intelligence

Artificial intelligence research at Tufts Computer Science is centered at the Machine Learning Group and the Human-Robot Interaction Lab with some collaboration between the labs. Our work spans many aspects of AI:

  • Learning: including foundations and algorithms for machine learning and data mining, interdisciplinary applications, and learning from natural language instructions 
  • Planning: including deterministic and decision-theoretic planning, learning for planning, and applying planning to robotic domains 
  • Knowledge representation and inference: including representation, inference algorithms and complexity analysis for propositional problems, and for relational structured domain
  • Natural language understanding: including parsing, semantic and pragmatic analysis, and dialogue processing 
  • Agent architectures: for simulated and robotic agents, including investigations of architectural tradeoffs and novel architectural mechanisms and algorithms for introspection and reflection, fault detection and recovery 
  • Cognitive architectures: for complex computational models of human cognitive functions and for complex artificial agents that interact with humans in natural language
  • Multi-agent systems: including computational middle-ware for artificial virtual and robotic agents, as well as grid-based computational infrastructures and simulation environments
  • Human-robot interaction: including empirical investigations and evaluations of robots interacting with humans in a variety of tasks, using natural language as well as brain-computer interfaces
  • Robot/machine ethics: including foundational work on potential dangers of technology as well as empirical human-robot interaction studies

Ongoing projects: 

  • "Towards an Integrated Cognitive Computational Architecture for Situated Natural Language Understanding and Reasoning" (with U of Miami)
  • "Effective Human-Robot Interaction under Time Pressure through Robust Natural Language Dialogue and Dynamic Autonomy" (with Notre Dame and Arizona State University)
  • "Effective Human-Robot Interaction with UAVs and USVs through Robust Natural Language Dialogue and Dynamic Autonomy"
  • "Integrated Situated Visual Scene and Natural Language Understanding for Human-Robot Interaction" (with TU Wien)
  • "Evidence-based Fusion of Hard and Soft Information for the Assessment of Reliability of Soft Information" (with U of Miami and Indiana U)
  • "Human-Robot Collaboration Based on a Hierarchy of Spatial Knowledge Representations" (with U of Michigan and UMass Lowell)
  • Bringing Brain-Computer Interfaces into Mainstream HCI
  • "Interdisciplinary Machine Learning Research and Education" (with Harvard)
  • "Optimizing Policies for Service Organizations in Complex Structured Domains" (with Oregon State University)

For more detailed information, project descriptions paper and so on please consult our group pages: Machine Learning Group and Human-Robot Interaction Lab.