We built a conversational agent performing social promotion (CASP) to assist in automation of interacting with Facebook friends. CASP relies on a domain-independent natural language relevance technique which filters web mining results to support a conversation with friends and other network members. In this study we focus on recognizing friends’ intents to better support automated conversation with them. We learn the plausible sequences of communicative actions and mental states as they are expressed in text to support plausible dialogue. We evaluate the relevance of the constructed conversations with respect to suitability of topicality and communicative actions, measuring how human users loose trust in the system. It is confirmed that maintaining a plausible sequences of communicative actions in automated postings is important for retaining trust of human peers and efficient social promotion by means of CASP.

This paper is concerned with extension of AngledCORE-9 by Sokeh, Gould, and Renz, a comprehensive representation of spatial information that can be efficiently extracted from interacting objects present in video using their approximated bounding box. Spatial information is important for identification of relation between multiple objects, hence the work is a step forward for tasks such as semantics content analysis and visual information access. To that end we present an approach to incorporating the spatiotemporal volume of objects into AngledCORE-9. The approach is able to detect, track and segment object volumes from a video stream, based on which spatial information is identified in an efficient manner. Accurate spatial and temporal information can be obtained by precise representation of the shape region and the oriented bounding box. A human action classification task is adopted in order to assess the performance of the approach. The experiment with two challenging datasets indicates that the outcome of this approach is comparable to the state-of-the-art.

Sophisticated autonomous AI may need to base its behavior on fuzzy concepts such as well-being or rights. These concepts cannot be given an explicit formal definition, but obtaining desired behavior still requires a way to instill the concepts in an AI system. To solve the problem, we review evidence suggesting that the human brain generates its concepts using a relatively limited set of rules and mechanisms. This suggests that it might be feasible to build AI systems that use similar criteria for generating their own concepts, and could thus learn similar concepts as humans do. Major challenges to this approach include the embodied nature of human thought, evolutionary vestiges in cognition, the social nature of concepts, and the need to compare conceptual representations between humans and AI systems.

Our research focuses on the use of Planning & Scheduling (P&S) technology for a team of robots providing daily assistance to multiple elder adults living in retirement facilities. Multi-user assistance and group-based activities require robots to plan and schedule their human-robot interaction (HRI) activities based on the specific needs, time constraints, availability and preferences of the multiple users. In this paper, we introduce and implement a novel centralized system architecture that can manage real P&S scenarios with multiple socially assistive robots, multiple users and their individual schedules, and single- and multi-person assistive activities. We describe how the main components of the proposed P&S architecture are integrated to control the robots, and to generate and monitor sequences of temporally annotated activities using off-the-shelf temporal planners. We verify that the architecture can manage realistic scenarios with three assistive robots, twenty users, and several single- and group-based activity requests during a single day.

This article discusses the notion of context transfer in reinforcement learning tasks. Context transfer, as defined in this article, implies knowledge transfer between tasks that share the same environment's dynamics and reward function, but have different state and action spaces. For example, we have a working mobile robot in an environment. At some point, we decide to upgrade its sensors and/or actuators. Any change in these modules will result in a different description of the agent-environment model, and the trained knowledge is no longer applicable. We consider the tasks of the old and new robots, as the source and target tasks, respectively. The Markov decision process (MDP) of these tasks, under certain conditions, are called Q-transferable tasks, and the problem of knowledge transfer between them is called context transfer. We investigate the relation of the MDPs of these tasks.

This paper outlines a new approach to creating value from the Smart Grid by incorporating individual households into the response system that must be deployed to accommodate increasingly large sources of intermittent renewable power. We propose a framework that couples agent-based AI techniques with envelope methods. Envelope methods provide a unified mathematical framework to model intermittent renewable resources, conventional dispatchable resources, demand side response, and storage. The overall goal of our system is to develop a distributed autonomous agent architecture that is able to facilitate market transactions among load serving entities, residential consumers, conventional merchant power producers, and intermittent power producers.

Growing demand is straining our existing electricity generation facilities and requires active participation of the utility and the consumers to achieve energy sustainability. One of the most effective and widely used ways to achieve this goal in the smart grid is demand response (DR), whereby consumers reduce their electricity consumption in response to a request sent from the utility whenever it anticipates a peak in demand. To successfully plan and implement demand response, the utility requires reliable estimate of reduced consumption during DR. This also helps in optimal selection of consumers and curtailment strategies during DR. While much work has been done on predicting normal consumption, reduced consumption prediction is an open problem that is under-studied. In this paper, we introduce and formalize the problem of reduced consumption prediction, and discuss the challenges associated with it. We also describe computational methods that use historical DR data as well as pre-DR conditions to make such predictions. Our experiments are conducted in the real-world setting of a university campus microgrid, and our preliminary results set the foundation for more detailed modeling.

Neurological studies (Rizzolatti and Craighero 2004) have shown that activity in the premotor In the United States, the rapid increase in the population of cortex may represent the integration of auditory information children with autism spectrum disorder (ASD) has revealed with temporally organized motor action during rhythmic the deficiency in the realm of therapeutic accessibility for cuing. Based on this theory, researchers have shown children with ASD in the domain of emotion and social that RAS can produce significant improvements in physical interaction. There have been a number of approaches including activities (Pacchetti et al. 2000). Given that music has several robotic therapeutic systems (Feil-Seifer and shown such a long history of therapeutic effects on psychological Mataric 2008; Scassellati, Admoni, and Mataric 2012) displaying (Siedliecki and Good 2006) and physical problems many intriguing strategies and meaningful results.

In this paper, we exploit Bayesian nonparametric microphone array processing (BNP-MAP) for analyzing the spatio-temporal patterns of the frog chorus. Such analysis in real environments is made more difficult due to unpredictable sound sources including calls of various species of animals. An application of conventional signal processing algorithms has been difficult because these algorithms usually require the number of sound sources in advance. BNP-MAP is developed to cope with auditory uncertainties such as reverberation or unknown number of sounds by using a unified model based on Bayesian nonparametrics. We exploit BNP-MAP for analyzing the sound data of 20 minutes captured by a 7-channel microphone array in a paddy rice field in Oki Island, Japan, and revealed that two individuals of Schlegel's green tree frog (Rhacophorus schlegelii) called alternately with anti-phase. This result is compared with the video data captured by a video camera with 18 units of sound-imaging devices called Firefly deployed along the bank of the rice field. The auditory result provides more detailed patterns of the frog chorus in higher temporal resolutions. This higher resolution enables to analyze fine temporal structures of the frog calls. For example, BNP-MAP reveals the trill-like calling pattern of R. schlegelii.

We introduce a novel feature set for reinforcement learning in visual domains (e.g. video games) designed to capture pairwise, position-invariant, spatial relationships between objects on the screen. The feature set is simple to implement and computationally practical, but nevertheless allows for substantial improvement over existing baselines in a wide variety of Atari 2600 games. In the most dramatic results the features allow multiple orders of magnitude improvement in performance.