Sense of agency (SoA) is the perceived control over one’s actions and their consequences, and through this one feels responsible for the consequent outcomes in the world. We analyze the far-reaching implications of a two-pronged knowledge on SoA and its impact on human-AI interactions. We argue that although there are interesting research efforts for an AI to inherently possess SoA, they are still sparse, constrained in scope and present unclear immediate benefit to the design of AI-enabled systems. We also argue that the knowledge on how human SoA is affected by an AI that is perceived to possess a sense of control presents more immediate benefit to AI, in particular, to eliciting positive human attitudes toward AI. Third, and lastly, we argue that research efforts for an AI to adapt to the dynamic changes of human SoA are practically non-existent primarily due to the difficulty of modeling, inferring and adaptively responding to human SoA in complex natural settings. We proceed by first delving deep into the influential and recent theoretical underpinnings of SoA, and discuss its conceptual reach in different disciplines and how it is applied in real-world research. We organize a substantial part of our paper to put forward and elucidate our three argumentative points while supported by evidence in the literature.

Previous studies have shown that the personality composition of a group significantly affects learners’ satisfaction during collaborative learning. However, while these studies investigated a group as a whole by focusing on group statistics, such as the mean and standard deviation of the members’ personalities, they paid little attention to the personality differences of individual pairs within the group, albeit the group contains many pairwise interactions. In this paper, we studied whether and how pairwise personality differences between a learner and groupmates affect the learner’s satisfaction. Examining data collected from an employee training program during which learners had reflective group discussions, we confirmed that pairwise personality differences significantly affect a learner’s level of satisfaction in the program. Specifically, satisfaction is affected by (1) the average of the personality differences between the learner and each individual groupmate, which reflects the degree to which the learner is different from the groupmates on average, and (2) the personality difference from the groupmate who has the most different/similar personality from/to the learner.

Sense of agency (SoA) is the subjective experience that one’s own volitional action caused an event to happen. This experience has cast light to understanding fundamental aspects of human behavior, which includes regulating actions during goal pursuit. Due to its many facets, investigating SoA has proved to be a strong challenge, compelling theorists and experimentalists to develop various paradigms to analyze it. While investigations on SoA have primarily focused on simple tasks that probe basic self-agency capacity awareness, and were carried out mostly under controlled laboratory settings over short experiment durations, we investigated this feeling of control in a complex, natural setting where participants performed daily their goal-directed tasks. More importantly, however, we investigated the SoA construct in a multidimensional way, i.e., simultaneously investigating its pre-reflective and reflective, local and general, and dynamic nature, as well as how individual differences moderated its influence on goal pursuit. We collected over 5,000 data points from 43 participants on their daily perceptions of self-agency and pursuance of healthy eating for more than a month outside the confines of a lab using a smartphone app that we designed. We present our analyses and insights that emerged from our empirical results on how the many facets of SoA impacted in various ways the pursuance of the goal. To our knowledge, we are the first to study the SoA construct in this manner, and we posit our method can be used for an intelligent system to enhance a human counterpart’s SoA for self-driven persuasion to follow through the goal.

In this paper, we propose an approach to predict customers’ personalities leveraging two modalities of customers’ data: (a) service usages logs of online services and (b) visual data collected in physical stores by surveillance cameras (e.g., gait and whereabouts). A number of companies provide services via online and offline nowadays, thus need to serve two different kinds of customers: “online customers,” who use services completely online and have only (a); and “offline customers,” who use only physical stores and have only (b). To improve personality prediction accuracy for these customers, our approach generates pseudo features of a non-existent modality from the other modality that the customers actually have (i.e., feature projection; e.g., generate pseudo visual data of the online customers from their real online service logs), and uses both pseudo and real features to predict their personalities. The evaluation using real-world data of a mobile carrier’s customers showed that our approach predicted personality more accurately than an ordinary unimodal approach for both online and offline customers. We also examined importance of the feature projection and compared two different projection methods.

The interpersonal compatibility of a company agent and a customer significantly affects the outcome of their communication. In existing research, however, compatibility has been studied only in terms of the similarity in personality and values. That is, agents and customers were compared only on the same dimensions that make up their personality and values, e.g., on the same trait of the Big Five (compared agent's Extraversion and customer's Extraversion) or same values as per Schwartz's Basic Values (agent's Conformity and customer's Conformity). In this paper, we studied compatibility from a broader perspective, i.e., in addition to the similarity, we investigated interactions across different dimensions (e.g., an agent's Extraversion and a customer's Conformity, or the former's Extraversion and the latter's Neuroticism). Examining 7,594 real call logs collected from telemarketing call centers, we have confirmed that such different dimensional interactions significantly affect a customer making a purchase (i.e., a customer's conversion) or not. A simulation where we matched agents and customers demonstrated that our compatibility model that incorporated the interactions across different dimensions yielded significant conversion lift, i.e., +46% on the average, compared from one that used only similarity in personality and values.

A new bandwidth-control design - called policer for packet probable discard (PPPD) - has been developed. This not only limits the bandwidth of each user to a policing bandwidth, but also achieves TCP throughputs equal to the policing bandwidth. The proposed PPPD uses an improved leaky bucket algorithm, by which packets are discarded at a certain probability when the received bandwidth is judged to be more than the policing bandwidth. Simulations show that the proposed PPPD increases TCP throughput from 74% to 95%, or more, of the policing bandwidth.

Decentralized and unstructured peer-to-peer (P2P) networks such as Gnutella are attractive for Internet-scale information retrieval and search systems because they require neither any centralized directory nor any centralized management of overlay network topology and data placement. However, due to this decentralized architecture, current P2P keyword search systems lack useful global knowledge such as popularity of data items and relationships between keywords and data items. As a result, current P2P keyword search systems supports only naive text-match search and can find only data items with a keyword (or meta-data) exactly indicated in a query. In this paper, we show an efficient P2P search system which increases possibility of discovering desired data items. The key mechanism is query expansion, where a received query is expanded based on keyword relationships managed in a distributed fashion by participating nodes. Keyword relationships are improved through search and retrieval processes and each relationships is shared among nodes holding similar data items. We also present implementation of our P2P search system.