Artificial Intelligence at Tufts

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

• "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.