Negotiation is ubiquitous in business applications in general and in procurement operations in particular. Nevertheless, several studies have shown that the negotiation process is often inefficient and time-consuming. In this paper, we propose a novel automated negotiation framework for procurement focusing on delivery date adjustment negotiations between buyers and suppliers. These negotiations are one of the most repeated negotiations in industrial applications.Nevertheless, they are often complex and time-consuming, as they involve multiple parties and require careful consideration of various internal and external factors. The proposed method was evaluated in the field and was shown to provide a significant reduction in the time required to reach achievement and around 95% closure rate. The proposed method is expected to be widely applicable in various procurement operations, contributing to improved efficiency and effectiveness in business negotiations. We also discuss the potential for future research in this area, including the integration of various machine learning techniques to further enhance the negotiation process.

As artificial intelligence (AI) becomes increasingly common in industry and business operations, researchers are focusing on how to enable AI agents representing different stakeholders to cooperate effectively in competitive environments. Inspired by human negotiation, automated negotiation is being explored as a solution. Traditional approaches like the Stacked Alternating Offers Protocol (SAOP) for multilateral negotiations and the Alternating Offers Protocol (AOP) for bilateral negotiations mimic human bargaining. Recently, the Tentative Acceptance Unique Offers Protocol (TAU) was proposed as an alternative that leads to faster and better agreements when a simple strategy is used by the negotiators. This paper introduces a method for adapting any existing AOP strategy to TAU. Our results demonstrate that this approach leads to faster and more beneficial agreements for all parties involved, with higher overall optimality and fairness. This improvement is achieved with a minor increase in information revelation.

With the availability of domain specific historical negotiation data, the practical applications of machine learning techniques can prove to be increasingly effective in the field of automated negotiation. Yet a large portion of the literature focuses on domain independent negotiation and thus passes the possibility of leveraging any domain specific insights from historical data. Moreover, during sequential negotiation, utility functions may alter due to various reasons including market demand, partner agreements, weather conditions, etc. This poses a unique set of challenges and one can easily infer that one strategy that fits all is rather impossible in such scenarios. In this work, we present a simple yet effective method of learning an end-to-end negotiation strategy from historical negotiation data. Next, we show that transfer learning based solutions are effective in designing adaptive strategies when underlying utility functions of agents change. Additionally, we also propose an online method of detecting and measuring such changes in the utility functions. Combining all three contributions we propose an adaptive automated negotiating agent framework that enables the automatic creation of transfer learning based negotiating agents capable of adapting to changes in utility functions. Finally, we present the results of an agent generated using our framework in different ANAC domains with 100 different utility functions each and show that our agent outperforms the benchmark score by domain independent agents by 6%.

The task of an automatic language identification (LID) system is to automatically identify the language in a spoken utterance. Language identification can be applied as front-end to speech-to-speech translation systems, in speaker diarization, and at call centers to automatically route incoming calls to appropriate native speaker operators. In the current study, a method for automatic language identification based on i-vector paradigm and conditional random fields (CRF) is presented. CRF belong to discriminative classifiers and use an exponential distribution to model a sequence given the observation sequence. This allows non-independent observations, and allows also non-local dependencies between state and observation. When the proposed method is evaluated on the NIST 2015 i-vector Machine Learning Challenge task for the recognition of 50 in-set languages, a 3.7% equal error rate (EER) (i.e., miss probability equal to false alarms) is achieved. Using support vector machines (SVM) a 5.2% EER, using probabilistic linear discriminant analysis (PLDA) a 6.7% EER, and when using convolutional neural networks (CNN) a 4.2% EER are achieved.

Automatic negotiation is gaining more interest recently thanks to the wider deployment of intelligent systems and the need for them to cooperate/compete on behalf of their users. A central assumption of most autonomous negotiation agents is that the utility function of the user is perfectly known to the agent. That is an often unmet assumption in real situations. Utility elicitation is the process of learning about the utility function of the user incrementally and has a long history in decision support research. Recently, some utility elicitation systems capable of incrementally eliciting the utility function of the user during the negotiation were presented. This work expands this body of research by optimizing the elicitation algorithm to realistic elicitation strategies. The proposed method extends the optimal elicitation algorithm to the - practical - case where queries to the user only reduce the uncertainty in the utility function without removing it completely. Extensive evaluation shows that the proposed extension outperforms two state-of-the-art elicitation algorithms and several baseline alternatives.

Activity recognition from smart devices and wearable sensors is an active area of research due to the widespread adoption of smart devices and the benefits it provide for supporting people in their daily lives. Many of the available datasets for fine-grained primitive activity recognition focus on locomotion or sports activities with less emphasis on real-world day-to-day behavior. This paper presents a new dataset for activity recognition in a realistic unmodified kitchen environment. Data was collected using only smart-watches from 10 lay participants while they prepared food in an unmodified rented kitchen. The paper also providing baseline performance measures for different classifiers on this dataset. Moreover, a deep feature learning system and more traditional statistical features based approaches are compared. This analysis shows that - for all evaluation criteria - data-driven feature learning allows the classifier to achieve best performance compared with hand-crafted features.

Our goal is to make a system to detect the times at which one almost laughed but he or she did not show their laughter on his/her face. We define this kind of laughter as hidden laughter. To accomplish this goal, we first tried making decision trees to detect one's amusement, the input data of which were physiological indices. We used 10-fold cross validation to evaluate the trees, and their accuracy was more than 70%. In addition, we investigated the effect of cultural background on the accuracy.

A promising technology for teaching robots new skills is learning from demonstrations (LfD) or imitation learning. Even though there is currently large literature in imitation learning, smaller attention was given to studying social aspects of the imitation situation. Back imitation is the situation when the demonstrator (human) imitates the learner (robot) before engaging in the actual teaching. In this paper we study the effect of back-imitation on human's perception of the robot's imitative skill. This study is motivated by the need to improve the acceptance of the robot through increasing its perceived imitation skill. Our claim is that back-imitation can be used to achieve this goal. The paper provides two studies involving 24 subjects and 36 HRI sessions that were conducted to investigate this claim. Statistical analysis of the results of these studies show that participants assigned the robots with which they engaged in back-imitation higher scores in terms of imitative skill and overall performance compared with robots that they did not imitate. The paper discusses the implications of this result for the design of imitation situations and broader implications for HRI research.

Abstract — Research in robot navigation usually concentrates on implementing navigation algorithms that allow the robot to navigate without human aid. In many real world situations, it is desirable that the robot is able to understand natural gestures from its user or partner and use this understanding to guide its navigation. Some algorithms already exist for learning natural gestures and/or their associated actions but most of these systems does not allow the robot to automatically generate the associated controller that allows it to actually navigate in the real environment. Furthermore, a technique is needed to combine the gestures/actions learned from interacting with multiple users or partners. This paper resolves these two issues and provides a complete system that allows the robot to learn interaction protocols and act upon them using only unsupervised learning techniques and enables it to combine the protocols learned from multiple users/partners. The proposed approach is general and can be applied to other interactive tasks as well. This paper also provides a real world experiment involving 18 subjects and 72 sessions that supports the ability of the proposed system to learn the needed gestures and to improve its knowledge of different gestures and their associations to actions over time. I.

Human-robot interaction using free hand gestures is gaining more importance as more untrained humans are operating robots in home and office environments. The robot needs to solve three problems to be operated by free hand gestures: gesture (command) detection, action generation (related to the domain of the task) and association between gestures and actions. In this paper we propose a novel technique that allows the robot to solve these three problems together learning the action space, the command space, and their relations by just watching another robot operated by a human operator. The main technical contribution of this paper is the introduction of a novel algorithm that allows the robot to segment and discover patterns in its perceived signals without any prior knowledge of the number of different patterns, their occurrences or lengths. The second contribution is using a Ganger-causality based test to limit the search space for the delay between actions and commands utilizing their relations and taking into account the autonomy level of the robot. The paper also presents a feasibility study in which the learning robot was able to predict actor's behavior with 95.2% accuracy after monitoring a single interaction between a novice operator and a WOZ operated robot representing the actor.

Mutual adaptation is an important phenomenon in human-human communications. Traditionally HRI research was more interested in investigating adaptation of the robot to the human using machine learning techniques but the possibility of utilizing the natural ability of humans to adapt to other humans and artifacts including robots is recently becoming increasingly attractive. This paper presents some of the results from an experiment conducted to investigate the interaction patterns and effectiveness of motion cues as a feedback modality between a human operator and a miniature robot in a confined collaborative navigation task. The results presented in this paper show evidence of human adaptation to the robot and moreover suggest that the adaptation rate is not constant or continuous in time but is discontinuous and nonlinear. The results also show evidence of a starting exploration stage before the adaptation with duration dependent on the expectations of the human regarding the capabilities of the robot in the given task. The paper investigates how to utilize these and related findings for building robots not only capable of adapting to human operators but can also help those operators adapt to them.

Drawing is a very natural activity of humans, and, despite the wide variety of drawing systems available on computers today, most of those systems lack the naturalness of the pencil-paper system. In this paper we present a new drawing interface that is easy for the human to use in a more natural way than the existing drawing interfaces. The proposed system is based on the Interactive Perception paradigm we developed for interfacing social robots to the humans.