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Demonstrations


ULTRA: A University-Lead Team Builder from RFPs and AnalysisRecommender Systems

We introduce ULTRA, a novel AI-based system for assisting team formation when researchers respond to calls for proposals from funding agencies.

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PRUDENT: A Generic Dialog Agent for Information Retrieval Based on Automated Planning Within a Reinforcement Learning Platform Automated Planning

We present a generic approach for dialogs for information retrieval based on automated planning within a reinforcement learning (RL)-based platform, ParlAI.

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ALLURE: A Multi-modal Guided Environment for Helping Children Learn to Solve a Rubik’s CubeNeuro-Symbolic AI

We demonstrate ALLURE, an educational AI system for learning to solve the Rubik’s cube that is designed to help students improve their problem solving skills.

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KITE: An Unsupervised, Effective and Inclusive Approach for Textual Content ExplorationData Analysis

We present an unsupervised system, called KITE, for exploring textual data which can generate insights from a general as well as domaindependent perspective consisting of holistic views, entity-centric view, events view, domain-specific interpretation using industry taxonomies and a detailed full-text view transparently connecting the document to insight elements.

[Paper] [Demo Video] [Tool] [BibTex]


EDU ChatbotChatbots

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ElectionBot for South CarolinaChatbots

[Paper] [Tool] [BibTex]


ElectionBot for MississippiChatbots

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Plansformer Tool: Demonstrating Generation of Symbolic Plans using Transformers, 2023Automated Planning

We introduce Plansformer; an LLM fine-tuned on planning problems and capable of generating plans with favorable behavior in terms of correctness and length with reduced knowledge-engineering efforts.

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Solving the Rubik's Cube with a PDDL Planner, 2023Neural-Symbolic AI

In this demonstration, we introduce the first PDDL formulation for the 3-dimension RC and solve it with an off-the-shelf Fast-Downward planner. We also create a plan executor and visualizer to show how the plan achieves the intended goal.

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