Predicting ConceptNet Path Quality Using Crowdsourced Assessments of Naturalness

In many applications, it is important to characterize the way in which two concepts are semantically related. Knowledge graphs such as ConceptNet provide a rich source of information for such characterizations by encoding relations between concepts as edges in a graph. When two concepts are not directly connected by an edge, their relationship can still be described in terms of the paths that connect them. Unfortunately, many of these paths are uninformative and noisy, which means that the success of applications that use such path features crucially relies on their ability to select high-quality paths. In existing applications, this path selection process is based on relatively simple heuristics. In this paper we instead propose to learn to predict path quality from crowdsourced human assessments. Since we are interested in a generic task-independent notion of quality, we simply ask human participants to rank paths according to their subjective assessment of the paths' naturalness, without attempting to define naturalness or steering the participants towards particular indicators of quality. We show that a neural network model trained on these assessments is able to predict human judgments on unseen paths with near optimal performance. Most notably, we find that the resulting path selection method is substantially better than the current heuristic approaches at identifying meaningful paths.Comment: In Proceedings of the Web Conference (WWW) 201

Towards Interpretable Explanations for Transfer Learning in Sequential Tasks

People increasingly rely on machine learning (ML) to make intelligent decisions. However, the ML results are often difficult to interpret and the algorithms do not support interaction to solicit clarification or explanation. In this paper, we highlight an emerging research area of interpretable explanations for transfer learning in sequential tasks, in which an agent must explain how it learns a new task given prior, common knowledge. The goal is to enhance a user’s ability to trust and use the system output and to enable iterative feedback for improving the system. We review prior work in probabilistic systems, sequential decision-making, interpretable explanations, transfer learning, and interactive machine learning, and identify an intersection that deserves further research focus. We believe that developing adaptive, transparent learning models will build the foundation for better human-machine systems in applications for elder care, education, and health care

Fairness in Multi-Agent Sequential Decision-Making

We define a fairness solution criterion for multi-agent decision-making problems, where agents have local interests. This new criterion aims to maximize the worst performance of agents with consideration on the overall performance. We develop a simple linear programming approach and a more scalable game-theoretic approach for computing an optimal fairness policy. This game-theoretic approach formulates this fairness optimization as a two-player, zero-sum game and employs an iterative algorithm for finding a Nash equilibrium, corresponding to an optimal fairness policy. We scale up this approach by exploiting problem structure and value function approximation. Our experiments on resource allocation problems show that this fairness criterion provides a more favorable solution than the utilitarian criterion, and that our game-theoretic approach is significantly faster than linear programming

On fairness in decision-making under uncertainty: Definitions, computation, and comparison

The utilitarian solution criterion, which has been extensively studied in multi-agent decision making under uncertainty, aims to maximize the sum of individual utilities. However, as the utilitarian solution often discriminates against some agents, it is not desirable for many practical applications where agents have their own interests and fairness is expected. To address this issue, this paper introduces egalitarian solution criteria for sequential decision-making under uncertainty, which are based on the maximin principle. Motivated by different application domains, we propose four maximin fairness criteria and develop corresponding algorithms for computing their optimal policies. Furthermore, we analyze the connections between these criteria and discuss and compare their characteristics

Pose consensus based on dual quaternion algebra with application to decentralized formation control of mobile manipulators

This paper presents a solution based on dual quaternion algebra to the general problem of pose (i.e., position and orientation) consensus for systems composed of multiple rigid-bodies. The dual quaternion algebra is used to model the agents' poses and also in the distributed control laws, making the proposed technique easily applicable to time-varying formation control of general robotic systems. The proposed pose consensus protocol has guaranteed convergence when the interaction among the agents is represented by directed graphs with directed spanning trees, which is a more general result when compared to the literature on formation control. In order to illustrate the proposed pose consensus protocol and its extension to the problem of formation control, we present a numerical simulation with a large number of free-flying agents and also an application of cooperative manipulation by using real mobile manipulators

Human-robot cross-training: Computational formulation, modeling and evaluation of a human team training strategy

We design and evaluate human-robot cross-training, a strategy widely used and validated for effective human team training. Cross-training is an interactive planning method in which a human and a robot iteratively switch roles to learn a shared plan for a collaborative task. We first present a computational formulation of the robot's interrole knowledge and show that it is quantitatively comparable to the human mental model. Based on this encoding, we formulate human-robot cross-training and evaluate it in human subject experiments (n = 36). We compare human-robot cross-training to standard reinforcement learning techniques, and show that cross-training provides statistically significant improvements in quantitative team performance measures. Additionally, significant differences emerge in the perceived robot performance and human trust. These results support the hypothesis that effective and fluent human-robot teaming may be best achieved by modeling effective practices for human teamwork.ABB Inc.U.S. Commercial Regional CenterAlexander S. Onassis Public Benefit Foundatio

Automatic prediction of consistency among team members' understanding of group decisions in meetings

Occasionally, participants in a meeting can leave with different understandings of what had been discussed. For meetings that require immediate response (such as disaster response planning), the participants must share a common understanding of the decisions reached by the group to ensure successful execution of their mission. In such domains, inconsistency among individuals' understanding of the meeting results would be detrimental, as this can potentially degrade group performance. Thus, detecting the occurrence of inconsistencies in understanding among meeting participants is a desired capability for an intelligent system that would monitor meetings and provide feedback to spur stronger group understanding. In this paper, we seek to predict the consistency among team members' understanding of group decisions. We use self-reported summaries as a representative measure for team members' understanding following meetings, and present a computational model that uses a set of verbal and nonverbal features from natural dialogue. This model focuses on the conversational dynamics between the participants, rather than on what is being discussed. We apply our model to a real-world conversational dataset and show that its features can predict group consistency with greater accuracy than conventional dialogue features. We also show that the combination of verbal and nonverbal features in multimodal fusion improves several performance metrics, and that our results are consistent across different meeting phases.National Science Foundation (U.S.). Graduate Research Fellowship Program (2012150705

Challenges in Developing a Collaborative Robotic Assistant for Automotive Assembly Lines

Industrial robots are on the verge of emerging from their cages, and entering the final assembly to work along side humans. Towards this we are developing a collaborative robot capable of assisting humans in the final automotive assembly. Several algorithmic as well as design challenges exist when the robots enter the unpredictable, human-centric and time-critical environment of final assembly. In this work, we briefly discuss a few of these challenges along with developed solutions and proposed methodologies, and their implications for improving human-robot collaboration

ConTaCT: Deciding to Communicate during Time-Critical Collaborative Tasks in Unknown, Deterministic Domains

Communication between agents has the potential to improve team performance of collaborative tasks. However, communication is not free in most domains, requiring agents to reason about the costs and benefits of sharing information. In this work, we develop an online, decentralized communication policy, ConTaCT, that enables agents to decide whether or not to communicate during time-critical collaborative tasks in unknown, deterministic environments. Our approach is motivated by real-world applications, including the coordination of disaster response and search and rescue teams. These settings motivate a model structure that explicitly represents the world model as initially unknown but deterministic in nature, and that de-emphasizes uncertainty about action outcomes. Simulated experiments are conducted in which ConTaCT is compared to other multi-agent communication policies, and results indicate that ConTaCT achieves comparable task performance while substantially reducing communication overhead