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. To set the stage for this union, we explore two different flavors of model space problems that have been studied in the AI planning literature and explore the effect of an LLM on those tasks. We empirically demonstrate how the performance of an LLM contrasts with combinatorial search (CS) – an approach that has been traditionally used to solve model space tasks in planning, both with the LLM in the role of a standalone model space reasoner as well as in the role of a statistical signal in concert with the CS approach as part of a two-stage process. Our experiments show promising results suggesting further forays of LLMs into the exciting world of model space reasoning for planning tasks in the future.

We present Lemming – a visualization tool for the interactive selection of plans for a given problem, allowing the user to efficiently whittle down the set of plans and select their plan(s) of choice. We demonstrate four different user experiences for this process, three of them based on the principle of using disjunctive action landmarks as guidance to cut down the set of choice points for the user, and one on the use of linear temporal logic (LTL) to impart additional constraints into the plan set using natural language (NL) instruction.

This is a demonstration of our newly released Python package NL2LTL which leverages the latest in natural language understanding (NLU) and large language models (LLMs) to translate natural language instructions to linear temporal logic (LTL) formulas. This allows direct translation to formal languages that a reasoning system can use, while at the same time, allowing the end-user to provide inputs in natural language without having to understand any details of an underlying formal language. The package comes with support for a set of default LTL patterns, corresponding to popular DECLARE templates, but is also fully extensible to new formulas and user inputs. The package is open-source and is free to use for the AI community under the MIT license. Open Source: https://github.com/IBM/nl2ltl. Video Link: https://bit.ly/3dHW5b1

While natural language systems continue improving, they are still imperfect. If a user has a better understanding of how a system works, they may be able to better accomplish their goals even in imperfect systems. We explored whether explanations can support effective authoring of natural language utterances and how those explanations impact users’ mental models in the context of a natural language system that generates small programs. Through an online study (n=252), we compared two main types of explanations: 1) system-focused, which provide information about how the system processes utterances and matches terms to a knowledge base, and 2) social, which provide information about how other users have successfully interacted with the system. Our results indicate that providing social suggestions of terms to add to an utterance helped users to repair and generate correct flows more than system-focused explanations or social recommendations of words to modify. We also found that participants commonly understood some mechanisms of the natural language system, such as the matching of terms to a knowledge base, but they often lacked other critical knowledge, such as how the system handled structuring and ordering. Based on these findings, we make design recommendations for supporting interactions with and understanding of natural language systems.

Virtual, Augmented, and Mixed Reality for Human-Robot Interaction (VAM-HRI) has been gaining considerable attention in HRI research in recent years. However, the HRI community lacks a set of shared terminology and framework for characterizing aspects of mixed reality interfaces, presenting serious problems for future research. Therefore, it is important to have a common set of terms and concepts that can be used to precisely describe and organize the diverse array of work being done within the field. In this article, we present a novel taxonomic framework for different types of VAM-HRI interfaces, composed of four main categories of virtual design elements (VDEs). We present and justify our taxonomy and explain how its elements have been developed over the past 30 years as well as the current directions VAM-HRI is headed in the coming decade.

Web applications and services are increasingly important in a distributed internet filled with diverse cloud services and applications, each of which enable the completion of narrowly defined tasks. Given the explosion in the scale and diversity of such services, their composition and integration for achieving complex user goals remains a challenging task for end-users and requires a lot of development effort when specified by hand. We present a demonstration of the Goal Oriented Flow Assistant (GOFA) system, which provides a natural language solution to generate workflows for application integration. Our tool is built on a three-step pipeline: it first uses Abstract Meaning Representation (AMR) to parse utterances; it then uses a knowledge graph to validate candidates; and finally uses an AI planner to compose the candidate flow. We provide a video demonstration of the deployed system as part of our submission.

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. We show how this formulation allows agents to not only leverage existing strategies for handling model differences like explanations (Chakraborti et al. 2017) and explicability (Kulkarni et al. 2019), but can also exhibit novel behaviors that are generated through the combination of these different strategies. Our formulation also reveals a deep connection to existing approaches in epistemic planning. Specifically, we show how we can leverage classical planning compilations for epistemic planning to solve Expectation-Aware planning problems. To the best of our knowledge, the proposed formulation is the first complete solution to planning with diverging user expectations that is amenable to a classical planning compilation while successfully combining previous works on explanation and explicability. We empirically show how our approach provides a computational advantage over our earlier approaches that rely on search in the space of models.

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. We hope that the survey will provide guidance to new researchers in automated planning towards the role of explanations in the effective design of human-in-the-loop systems, as well as provide the established researcher with some perspective on the evolution of the exciting world of explainable planning.

Designing robots capable of generating interpretable behavior is essential for effective human-robot collaboration. This requires robots to be able to generate behavior that aligns with human expectations but exhibiting such behavior in arbitrary environments could be quite expensive for robots, and in some cases, the robot may not even be able to exhibit expected behavior. However, in structured environments (like warehouses, restaurants, etc.), it may be possible to design the environment so as to boost the interpretability of a robot's behavior or to shape the human's expectations of the robot's behavior. In this paper, we investigate the opportunities and limitations of environment design as a tool to promote a particular type of interpretable behavior - known in the literature as explicable behavior. We formulate a novel environment design framework that considers design over multiple tasks and over a time horizon. In addition, we explore the longitudinal effect of explicable behavior and the trade-off that arises between the cost of design and the cost of generating explicable behavior over an extended time horizon.

Proactive Decision Support aims at improving the decision making experience of human decision-makers by enhancing the quality of the decisions and the ease of making them. Given that AI techniques are efficient in searching over a potentially large solution space (of decision) and finding good solutions, it can be used for human-in-the-loop scenarios such as disaster response that demand naturalistic decision making. A human decision-maker, in such scenarios, may experience high-cognitive overload leading to a loss of situational awareness. In this paper, we propose the use of automated task-planning techniques coupled with design principles laid out in the Human-Computer Interaction (HCI) community for developing a proactive decision support system. To this extent, we highlight the capabilities of such a system RADAR and briefly, describe how automated planning techniques help us in providing the varying degrees of assistance. To evaluate the effectiveness of the different capabilities, we conduct ablation studies with human subjects on a synthetic environment for making an interactive plan of study. We found that planning techniques like plan validation and suggestions help to reduce planning time (objective metrics) and improves user satisfaction (subjective metrics) compared to expert human planners without any support.

The state of the art in automated conversational agents for enterprise (e.g. for customer support) require a lengthy design process with experts in the loop who have to figure out and specify complex conversation patterns. This demonstration looks at a prototype interface that aims to bring down the expertise required to design such agents as well as the time taken to do so. Specifically, we will focus on how a metawriter can assist the domain-writer during the design process and how complex conversation patterns can be derived from simplifying abstractions at the interface level.

The ability of an AI agent to build mental models can open up pathways for manipulating and exploiting the human in the hopes of achieving some greater good. In fact, such behavior does not necessarily require any malicious intent but can rather be borne out of cooperative scenarios. It is also beyond the scope of misinterpretation of intents, as in the case of value alignment problems, and thus can be effectively engineered if desired (i.e. algorithms exist that can optimize such behavior not because models were misspecified but because they were misused). Such techniques pose several unresolved ethical and moral questions with regards to the design of autonomy. In this paper, we illustrate some of these issues in a teaming scenario and investigate how they are perceived by participants in a thought experiment. Finally, we end with a discussion on the moral implications of such behavior from the perspective of the doctor-patient relationship.

There has recently been an explosion of work in the human-robot interaction (HRI) community on the use of mixed, augmented, and virtual reality. We present a novel conceptual framework to characterize and cluster work in this new area and identify gaps for future research. We begin by introducing the Plane of Interaction: a framework for characterizing interactive technologies in a 2D space informed by the Model-View-Controller design pattern. We then describe how Interactive Design Elements that contribute to the interactivity of a technology can be characterized within this space and present a taxonomy of mixed-reality interactive design elements. We then discuss how these elements may be rendered onto both reality- and virtuality-based environments using a variety of hardware devices and introduce the Reality-Virtuality Interaction Cube: a three-dimensional continuum representing the design space of interactive technologies formed by combining the Plane of Interaction with the Reality-Virtuality Continuum. Finally, we demonstrate the feasibility and utility of this framework by clustering and analyzing the set of papers presented at the 2018 VAM-HRI workshop.

The 2ndInternational Workshop on Virtual, Augmented, and Mixed Reality for Human-Robot Interactions (VAM-HRI) will bring together HRI, Robotics, and Mixed Reality researchers to identify challenges in mixed reality interactions between humans and robots. Topics relevant to the workshop include development of robots that can interact with humans in mixed reality, use of virtual reality for developing interactive robots, the design of new augmented reality interfaces that mediate communication between humans and robots, comparisons of the capabilities and perceptions of robots and virtual agents, and best design practices. VAM-HRI was held for the first time at HRI 2018, where it served as the first workshop of its kind at an academic AI or Robotics conference, and served as a timely call to arms to the academic community in response to the growing promise of this emerging field. VAM-HRI 2019 will follow on the success of VAM-HRI 2018, and present new opportunities for expanding this nascent research community.

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. Existing algorithms in such settings, while having been built on contrastive, selective and social properties of explanations as studied extensively in the psychology literature, have not, to the best of our knowledge, been evaluated in settings with actual humans in the loop. As such, the applicability of such explanations to human-AI and human-robot interactions remains suspect. In this paper, we set out to evaluate these explanation generation algorithms in a series of studies in a mock search and rescue scenario with an internal semi-autonomous robot and an external human commander. During that process, we hope to demonstrate to what extent the properties of these algorithms hold as they are evaluated by humans.

Recent work has formalized the explanation process in the context of automated planning as one of model reconciliation - i.e. a process by which the planning agent can bring the explainee's (possibly faulty) model of a planning problem closer to its understanding of the ground truth until both agree that its plan is the best possible. The content of explanations can thus range from misunderstandings about the agent's beliefs (state), desires (goals) and capabilities (action model). Though existing literature has considered different kinds of these model differences to be equivalent, literature on the explanations in social sciences has suggested that explanations with similar logical properties may often be perceived differently by humans. In this brief report, we explore to what extent humans attribute importance to different kinds of model differences that have been traditionally considered equivalent in the model reconciliation setting. Our results suggest that people prefer the explanations which are related to the effects of actions.

Human-aware planning involves generating plans that are explicable as well as providing explanations when such plans cannot be found. In this paper, we bring these two concepts together and show how an agent can achieve a trade-off between these two competing characteristics of a plan. In order to achieve this, we conceive a first of its kind planner MEGA that can augment the possibility of explaining a plan in the plan generation process itself. We situate our discussion in the context of recent work on explicable planning and explanation generation and illustrate these concepts in two well-known planning domains, as well as in a demonstration of a robot in a typical search and reconnaissance task. Human factor studies in the latter highlight the usefulness of the proposed approach.

In this demonstration, we report on the visualization capabilities of an Explainable AI Planning (XAIP) agent that can support human-in-the-loop decision-making. Imposing transparency and explainability requirements on such agents is crucial for establishing human trust and common ground with an end-to-end automated planning system. Visualizing the agent's internal decision making processes is a crucial step towards achieving this. This may include externalizing the "brain" of the agent: starting from its sensory inputs, to progressively higher order decisions made by it in order to drive its planning components. We demonstrate these functionalities in the context of a smart assistant in the Cognitive Environments Laboratory at IBM's T.J. Watson Research Center.

Recent advances in mixed-reality technologies have renewed interest in alternative modes of communication for human-robot interaction. However, most of the work in this direction has been confined to tasks such as teleoperation, simulation or explication of individual actions of a robot. In this paper, we will discuss how the capability to project intentions affect the task planning capabilities of a robot. Specifically, we will start with a discussion on how projection actions can be used to reveal information regarding the future intentions of the robot at the time of task execution. We will then pose a new planning paradigm - projection-aware planning - whereby a robot can trade off its plan cost with its ability to reveal its intentions using its projection actions. We will demonstrate each of these scenarios with the help of a joint human-robot activity using the HoloLens.

Intelligent robots and machines are becoming pervasive in human populated environments. A desirable capability of these agents is to respond to goal-oriented commands by autonomously constructing task plans. However, such autonomy can add significant cognitive load and potentially introduce safety risks to humans when agents behave in unexpected ways. Hence, for such agents to be helpful, one important requirement is for them to synthesize plans that can be easily understood by humans. While there exists previous work that studied socially acceptable robots that interact with humans in “natural ways”, and work that investigated legible motion planning, there is no general solution for high level task planning. To address this issue, we introduce the notions of plan explicability and predictability. To compute these measures, first, we postulate that humans understand agent plans by associating abstract tasks with agent actions, which can be considered as a labeling process. We learn the labeling scheme of humans for agent plans from training examples using conditional random fields (CRFs). Then, we use the learned model to label a new plan to compute its explicability and predictability. These measures can be used by agents to proactively choose or directly synthesize plans that are more explicable and predictable to humans. We provide evaluations on a synthetic domain and with a physical robot to demonstrate the effectiveness of our approach.

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.

In this brief abstract, we develop a computationally simpler version of the operator count heuristic for a particular class of domains. The contribution of this abstract is thus threefold, we (1) propose an efficient closed form approximation to the operator count heuristic; (2) leverage compressed sensing techniques to obtain an integer approximation in polynomial time; and (3) discuss the relationship of the proposed formulation to existing heuristics and investigate properties of domains where such approaches are useful.

Recently there has been a lot of focus on human robot co-habitation issues that are often orthogonal to many aspects of human-robot teaming; e.g. on producing socially acceptable behaviors of robots and de-conflicting plans of robots and humans in shared environments. However, an interesting offshoot of these settings that has largely been overlooked is the problem of planning for serendipity - i.e. planning for stigmergic collaboration without explicit commitments on agents in co-habitation. In this paper we formalize this notion of planning for serendipity for the first time, and provide an Integer Programming based solution for this problem. Further, we illustrate the different modes of this planning technique on a typical Urban Search and Rescue scenario and show a real-life implementation of the ideas on the Nao Robot interacting with a human colleague.

It has long been assumed that for effective human-robot teaming, it is desirable for assistive robots to infer the goals and intents of the humans, and take proactive actions to help them achieve their goals. However, there has not been any systematic evaluation of the accuracy of this claim. On the face of it, there are several ways a proactive robot assistant can in fact reduce the effectiveness of teaming. For example, it can increase the cognitive load of the human teammate by performing actions that are unanticipated by the human. In such cases, even though the teaming performance could be improved, it is unclear whether humans are willing to adapt to robot actions or are able to adapt in a timely manner. Furthermore, misinterpretations and delays in goal and intent recognition due to partial observations and limited communication can also reduce the performance. In this paper, our aim is to perform an analysis of human factors on the effectiveness of such proactive support in human-robot teaming. We perform our evaluation in a simulated Urban Search and Rescue (USAR) task, in which the efficacy of teaming is not only dependent on individual performance but also on teammates' interactions with each other. In this task, the human teammate is remotely controlling a robot while working with an intelligent robot teammate `Mary'. Our main result shows that the subjects generally preferred Mary with the ability to provide proactive support (compared to Mary without this ability). Our results also show that human cognitive load was increased with a proactive assistant (albeit not significantly) even though the subjects appeared to interact with it less.

One subclass of human computation applications are those directed at tasks that involve planning (e.g. tour planning) and scheduling (e.g. conference scheduling). Interestingly, work on these systems shows that even primitive forms of automated oversight on the human contributors helps in significantly improving the effectiveness of the humans/crowd. In this paper, we argue that the automated oversight used in these systems can be viewed as a primitive automated planner, and that there are several opportunities for more sophisticated automated planning in effectively steering the crowd. Straightforward adaptation of current planning technology is however hampered by the mismatch between the capabilities of human workers and automated planners. We identify and partially address two important challenges that need to be overcome before such adaptation of planning technology can occur: (i) interpreting inputs of the human workers (and the requester) and (ii) steering or critiquing plans produced by the human workers, armed only with incomplete domain and preference models. To these ends, we describe the implementation of AI-MIX, a tour plan generation system that uses automated checks and alerts to improve the quality of plans created by human workers; and present a preliminary evaluation of the effectiveness of steering provided by automated planning.

Human computation applications that involve planning and scheduling are gaining popularity, and the existing literature on such systems shows that any automated oversight on human contributors improves the effectiveness of the crowd. In this paper, we present our ongoing work on the AI-MIX system, which is a first step towards using an automated planning and scheduling system in a crowdsourced planning application. In order to address the mismatch between the capabilities of the crowd and the automated planner, we identify two major challenges -- interpretation, and steering. We also present preliminary empirical results over the tour planning domain, and show how using an automated planner can help improve the quality of plans.

Beliefs play an important role in human-robot teaming scenarios, where the robots must reason about other agents' intentions and beliefs in order to inform their own plan generation process, and to successfully coordinate plans with the other agents. In this paper, we cast the evolving and complex structure of beliefs, and inference over them, as a planning and plan recognition problem. We use agent beliefs and intentions modeled in terms of predicates in order to create an automated planning problem instance, which is then used along with a known and complete domain model in order to predict the plan of the agent whose beliefs are being modeled. Information extracted from this predicted plan is used to inform the planning process of the modeling agent, to enable coordination. We also look at an extension of this problem to a plan recognition problem. We conclude by presenting an evaluation of our technique through a case study implemented on a real robot.

In recent years there has been a growing trend in the application of Memetic Algorithms for solving numerical optimization problems. They are population based search heuristics that integrate the benefits of natural and cultural evolution. In this paper, we propose an Adaptive Memetic Algorithm, named LA-DE which employs a competitive variant of Differential Evolution for global search and Learning Automata as the local search technique. During evolution Stochastic Automata Learning helps to balance the exploration and exploitation capabilities of DE resulting in local refinement. The proposed algorithm has been evaluated on a test-suite of 25 benchmark functions provided by CEC 2005 special session on real parameter optimization. Experimental results indicate that LA-DE outperforms several existing DE variants in terms of solution quality.

This article provides a swarm intelligence approach to 3D face recognition. A parametric evolutionary face model is proposed and the optimal parameters are determined by minimizing an error function. The extracted parameters are employed in the recognition phase for classification. Experimental validations have been performed on the neutral face scans of the CASIA Face Database, a challenging database for face recognition purposes and the results demonstrate the efficacy of the approach.