Kambhampati, Subbarao


Extracting Action Sequences from Texts Based on Deep Reinforcement Learning

arXiv.org Artificial Intelligence

Extracting action sequences from natural language texts is challenging, as it requires commonsense inferences based on world knowledge. Although there has been work on extracting action scripts, instructions, navigation actions, etc., they require that either the set of candidate actions be provided in advance, or that action descriptions are restricted to a specific form, e.g., description templates. In this paper, we aim to extract action sequences from texts in free natural language, i.e., without any restricted templates, provided the candidate set of actions is unknown. We propose to extract action sequences from texts based on the deep reinforcement learning framework. Specifically, we view "selecting" or "eliminating" words from texts as "actions", and the texts associated with actions as "states". We then build Q-networks to learn the policy of extracting actions and extract plans from the labeled texts. We demonstrate the effectiveness of our approach on several datasets with comparison to state-of-the-art approaches, including online experiments interacting with humans.


Explicablility as Minimizing Distance from Expected Behavior

arXiv.org Artificial Intelligence

In order to have effective human AI collaboration, it is not simply enough to address the question of autonomy; an equally important question is, how the AI's behavior is being perceived by their human counterparts. When AI agent's task plans are generated without such considerations, they may often demonstrate inexplicable behavior from the human's point of view. This problem arises due to the human's partial or inaccurate understanding of the agent's planning process and/or the model. This may have serious implications on human-AI collaboration, from increased cognitive load and reduced trust in the agent, to more serious concerns of safety in interactions with physical agent. In this paper, we address this issue by modeling the notion of plan explicability as a function of the distance between a plan that agent makes and the plan that human expects it to make. To this end, we learn a distance function based on different plan distance measures that can accurately model this notion of plan explicability, and develop an anytime search algorithm that can use this distance as a heuristic to come up with progressively explicable plans. We evaluate the effectiveness of our approach in a simulated autonomous car domain and a physical service robot domain. We provide empirical evaluations that demonstrate the usefulness of our approach in making the planning process of an autonomous agent conform to human expectations.


RADAR — A Proactive Decision Support System for Human-in-the-Loop Planning

AAAI Conferences

Proactive Decision Support (PDS) aims at improving the decision making experience of  human decision makers by enhancing both the quality of the decisions and the ease of making them. In this paper, we ask the question what role automated decision-making technologies can play in the deliberative process of the human decision maker.Specifically, we focus on expert humans in the loop who now share a detailed, if not complete, model of the domain with the assistant, but may still be unable to compute plans due to cognitive overload. To this end, we propose a PDS framework RADAR based on research in the automated planning community that aids the human decision maker in constructing plans. We will situate our discussion on principles of interface design laid out in the literature on the degrees of automation and its effect on the collaborative decision-making process.  Also, at the heart of our design is the principle of  naturalistic decision making which has been shown to be a necessary requirement of such systems, thus focusing more on providing suggestions rather than enforcing decisions and executing actions. We will demonstrate the different properties of such a system through examples in a fire-fighting domain, where human commanders are involved in building response strategies to mitigate a fire outbreak.The paper is written to serve both as a position paper by motivating requirements of an effective proactive decision support system, and also an emerging application of these ideas in the context of the role of an automated planner in human decision making, in a platform that can prove to be a valuable test bed for research on the same.


Balancing Explicability and Explanation in Human-Aware Planning

AAAI Conferences

Human aware planning requires an agent to be aware of the intentions, capabilities and mental model of the human in the loop during its decision process.This can involve generating plans that are explicable to a human observer as well as the ability to provide explanations when such plans cannot be generated. This has led to the notion "multi-model planning'' which aim to incorporate effects of human expectation in the deliberative process of a planner — either in the form of explicable task planning or explanations produced thereof. 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.This in effect provides a comprehensive perspective of what it means for a decision making agent to be "human-aware" by bringing together existing principles of planning under the umbrella of a single plan generation process.We situate our discussion specifically keeping in mind the recent work on explicable planning and explanation generation, and illustrate these concepts in modified versions of two well known planning domains, as well as a demonstration on a robot involved in a typical search and reconnaissance task with an external supervisor.


Explanations as Model Reconciliation — A Multi-Agent Perspective

AAAI Conferences

In this paper, we demonstrate how a planner (or a robot as an embodiment of it) can explain its decisions to multiple agents in the loop together considering not only the model that it used to come up with its decisions but also the (often misaligned) models of the same task that the other agents might have had. To do this, we build on our previous work on multi-model explanation generation and extend it to account for settings where there is uncertainty of the robot's model of the explainee and/or there are multiple explainees with different models to explain to. We will illustrate these concepts in a demonstration on a robot involved in a typical search and reconnaissance scenario with another human teammate and an external human supervisor.


UbuntuWorld 1.0 LTS - A Platform for Automated Problem Solving & Troubleshooting in the Ubuntu OS

arXiv.org Artificial Intelligence

In this paper, we present UbuntuWorld 1.0 LTS - a platform for developing automated technical support agents in the Ubuntu operating system. Specifically, we propose to use the Bash terminal as a simulator of the Ubuntu environment for a learning-based agent and demonstrate the usefulness of adopting reinforcement learning (RL) techniques for basic problem solving and troubleshooting in this environment. We provide a plug-and-play interface to the simulator as a python package where different types of agents can be plugged in and evaluated, and provide pathways for integrating data from online support forums like AskUbuntu into an automated agent's learning process. Finally, we show that the use of this data significantly improves the agent's learning efficiency. We believe that this platform can be adopted as a real-world test bed for research on automated technical support.


Plan Explanations as Model Reconciliation: Moving Beyond Explanation as Soliloquy

arXiv.org Artificial Intelligence

When AI systems interact with humans in the loop, they are often called on to provide explanations for their plans and behavior. Past work on plan explanations primarily involved the AI system explaining the correctness of its plan and the rationale for its decision in terms of its own model. Such soliloquy is wholly inadequate in most realistic scenarios where the humans have domain and task models that differ significantly from that used by the AI system. We posit that the explanations are best studied in light of these differing models. In particular, we show how explanation can be seen as a "model reconciliation problem" (MRP), where the AI system in effect suggests changes to the human's model, so as to make its plan be optimal with respect to that changed human model. We will study the properties of such explanations, present algorithms for automatically computing them, and evaluate the performance of the algorithms.


A Formal Analysis of Required Cooperation in Multi-Agent Planning

AAAI Conferences

It is well understood that,through cooperation, multiple agents can achieve tasks that are unachievable by a single agent.However, there are no formal characterizations of situations where cooperation is required to achieve a goal, thus warranting the application of multiple agents. In this paper, we provide such a formal characterization for multi-agent planning problems with sequential action execution. We first show that determining whether there is required cooperation (RC) is in general intractable even in this limited setting. As a result, we start our analysis with a subset of more restrictive problems where agents are homogeneous.For such problems, we identify two conditions that can cause RC. We establish that when none of these conditions hold, the problem is single-agent solvable;otherwise, we provide upper bounds on the minimum number of agents required. For the remaining problems with heterogeneous agents, we further divide them into two subsets.For one of the subsets,we propose the concept of {\em transformer agent} to reduce the number of agents to be considered which is used to improve planning performance.We implemented a planner using our theoretical results and compared it with one of the best IPC CoDMAP planners in the centralized track.Results show that our planner provides significantly improved performance on IPC CoDMAP domains.


Tweeting the Mind and Instagramming the Heart: Exploring Differentiated Content Sharing on Social Media

AAAI Conferences

Understanding the usage of multiple Online Social Networks (OSNs) is of significant research interest as it helps in identifying the distinguishing traits of each social media platform that contribute to its continued existence. A comparison between two OSNs is particularly useful when it is done on the representative set of users holding active accounts on both the platforms. In this research, we collected a set of users holding accounts on both Twitter and Instagram. An extensive textual and visual analysis on the media content posted by these users reveals that these platforms are indeed perceived differently at a fundamental level with Instagram engaging more of the users' heart and Twitter capturing more of their mind. These differences get reflected in the linguistic, topical and visual aspects of the user posts.


A Combinatorial Search Perspective on Diverse Solution Generation

AAAI Conferences

Finding diverse solutions has become important in many combinatorial search domains, including Automated Planning, Path Planning and Constraint Programming. Much of the work in these directions has however focussed on coming up with appropriate diversity metrics and compiling those metrics in to the solvers/planners. Most approaches use linear-time greedy algorithms for exploring the state space of solution combinations for generating a diverse set of solutions, limiting not only their completeness but also their effectiveness within a time bound. In this paper, we take a combinatorial search perspective on generating diverse solutions. We present a generic bi-level optimization framework for finding cost-sensitive diverse solutions. We propose complete methods under this framework, which guarantee finding a set of cost sensitive diverse solutions satisficing the given criteria whenever there exists such a set. We identify various aspects that affect the performance of these exhaustive algorithms and propose techniques to improve them. Experimental results show the efficacy of the proposed framework compared to an existing greedy approach.