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Found 30 papers, 7 papers with code
TESS: A Multi-intent Parser for Conversational Multi-Agent Systems with Decentralized Natural Language Understanding Models
Chatbots have become one of the main pathways for the delivery of business automation tools.
Can LLMs Fix Issues with Reasoning Models? Towards More Likely Models for AI Planning
This is the first work to look at the application of large language models (LLMs) for the purpose of model space edits in automated planning tasks.
TOBY: A Tool for Exploring Data in Academic Survey Papers
This paper describes TOBY, a visualization tool that helps a user explore the contents of an academic survey paper.
MACQ: A Holistic View of Model Acquisition Techniques
For over three decades, the planning community has explored countless methods for data-driven model acquisition.
Virtual, Augmented, and Mixed Reality for Human-Robot Interaction: A Survey and Virtual Design Element Taxonomy
Virtual, Augmented, and Mixed Reality for Human-Robot Interaction (VAM-HRI) has been gaining considerable attention in research in recent years.
COVID-19 India Dataset: Parsing COVID-19 Data in Daily Health Bulletins from States in India
While India has been one of the hotspots of COVID-19, data about the pandemic from the country has proved to be largely inaccessible at scale.
NeurIPS 2020 NLC2CMD Competition: Translating Natural Language to Bash Commands
The NLC2CMD Competition hosted at NeurIPS 2020 aimed to bring the power of natural language processing to the command line.
A Bayesian Account of Measures of Interpretability in Human-AI Interaction
Existing approaches for the design of interpretable agent behavior consider different measures of interpretability in isolation.
Explainable Composition of Aggregated Assistants
A new design of an AI assistant that has become increasingly popular is that of an "aggregated assistant" -- realized as an orchestrated composition of several individual skills or agents that can each perform atomic tasks.
From Robotic Process Automation to Intelligent Process Automation: Emerging Trends
In this survey, we study how recent advances in machine intelligence are disrupting the world of business processes.
Designing Environments Conducive to Interpretable Robot Behavior
Given structured environments (like warehouses and restaurants), it may be possible to design the environment so as to boost the interpretability of the robot's behavior or to shape the human's expectations of the robot's behavior.
The Emerging Landscape of Explainable AI Planning and Decision Making
In this paper, we provide a comprehensive outline of the different threads of work in Explainable AI Planning (XAIP) that has emerged as a focus area in the last couple of years and contrast that with earlier efforts in the field in terms of techniques, target users, and delivery mechanisms.
Project CLAI: Instrumenting the Command Line as a New Environment for AI Agents
This whitepaper reports on Project CLAI (Command Line AI), which aims to bring the power of AI to the command line interface (CLI).
D3BA: A Tool for Optimizing Business Processes Using Non-Deterministic Planning
This paper builds upon recent work in the declarative design of dialogue agents and proposes an exciting new tool -- D3BA -- Declarative Design for Digital Business Automation, built to optimize business processes using the power of AI planning.
A Unified Conversational Assistant Framework for Business Process Automation
Business process automation is a booming multi-billion-dollar industry that promises to remove menial tasks from workers' plates -- through the introduction of autonomous agents -- and free up their time and brain power for more creative and engaging tasks.
Planning for Goal-Oriented Dialogue Systems
Generating complex multi-turn goal-oriented dialogue agents is a difficult problem that has seen a considerable focus from many leaders in the tech industry, including IBM, Google, Amazon, and Microsoft.
Expectation-Aware Planning: A Unifying Framework for Synthesizing and Executing Self-Explaining Plans for Human-Aware Planning
In this work, we present a new planning formalism called Expectation-Aware planning for decision making with humans in the loop where the human's expectations about an agent may differ from the agent's own model.
Explicability? Legibility? Predictability? Transparency? Privacy? Security? The Emerging Landscape of Interpretable Agent Behavior
There has been significant interest of late in generating behavior of agents that is interpretable to the human (observer) in the loop.
Plan Explanations as Model Reconciliation -- An Empirical Study
Recent work in explanation generation for decision making agents has looked at how unexplained behavior of autonomous systems can be understood in terms of differences in the model of the system and the human's understanding of the same, and how the explanation process as a result of this mismatch can be then seen as a process of reconciliation of these models.
Algorithms for the Greater Good! On Mental Modeling and Acceptable Symbiosis in Human-AI Collaboration
Effective collaboration between humans and AI-based systems requires effective modeling of the human in the loop, both in terms of the mental state as well as the physical capabilities of the latter.
Visualizations for an Explainable Planning Agent
In this paper, we report on the visualization capabilities of an Explainable AI Planning (XAIP) agent that can support human in the loop decision making.
Balancing Explicability and Explanation in Human-Aware Planning
In this paper, we bring these two concepts together and show how a planner can account for both these needs and achieve a trade-off during the plan generation process itself by means of a model-space search method MEGA.
AI Challenges in Human-Robot Cognitive Teaming
Among the many anticipated roles for robots in the future is that of being a human teammate.
MTDeep: Boosting the Security of Deep Neural Nets Against Adversarial Attacks with Moving Target Defense
Present attack methods can make state-of-the-art classification systems based on deep neural networks misclassify every adversarially modified test example.
Plan Explanations as Model Reconciliation: Moving Beyond Explanation as Soliloquy
When AI systems interact with humans in the loop, they are often called on to provide explanations for their plans and behavior.
Explicablility as Minimizing Distance from Expected Behavior
In order to have effective human-AI collaboration, it is necessary to address how the AI agent's behavior is being perceived by the humans-in-the-loop.
UbuntuWorld 1.0 LTS - A Platform for Automated Problem Solving & Troubleshooting in the Ubuntu OS
In this paper, we present UbuntuWorld 1. 0 LTS - a platform for developing automated technical support agents in the Ubuntu operating system.
Proactive Decision Support using Automated Planning
Proactive decision support (PDS) helps in improving the decision making experience of human decision makers in human-in-the-loop planning environments.
Compliant Conditions for Polynomial Time Approximation of Operator Counts
In this paper, we develop a computationally simpler version of the operator count heuristic for a particular class of domains.
Plan Explicability and Predictability for Robot Task Planning
Hence, for such agents to be helpful, one important requirement is for them to synthesize plans that can be easily understood by humans.
