We present a Companion-based Ambient Robust INtelliGence (CARING) system, for communication with, and support of, clients with Traumatic brain injury (TBI) or Amyotrophic Lateral Sclerosis (ALS). A central component of this system is an artificial companion, combined with a range of elements for ambient intelligence. The companion acts as a personalized intermediary for multi-party communication between the client, the environment (e.g. a Smart Home), caregivers and health professionals. CARING is based on tightly coupled systems drawing from natural language processing, speech recognition and adaptation, deep language understanding and constraint-based knowledge representation and reasoning. A major innovation of the system is its ability to adapt and accommodate different interfaces associated with different client capabilities and needs. The system will use, as a proxy, different interaction requirements of clients (e.g., Brain-Computer Interfaces) at different stages of ALS progression and with different types of TBI impairments. Ultimately, this technology is expected to improve the quality of life for clients through conversation with a computer.

This paper presents an exploratory study conducted to understand how audio-visual prompts are understood by people on an emotional level as a first step towards the more challenging task of designing emotionally aligned prompts for persons with cognitive disabilities such as Alzheimer’s disease and related dementias (ADRD). Persons with ADRD often need assistance from a caregiver to complete daily living activities such as washing hands, making food, or getting dressed. Artificially intelligent systems have been developed that can assist in such situations. This paper presents a set of prompt videos of a virtual human ‘Rachel’, wherein she expressively communicates prompts at each step of a simple hand washing task, with various human-like emotions and behaviors. A user study was conducted for 30 such videos with respect to three basic and important dimensions of emotional experience: evaluation, potency, and activity. The results show that, while people generally agree on the evaluation (valence: good/bad) of a prompt, consensus about power and activity is not as socially homogeneous. Our long term aim is to enhance such systems by delivering automated prompts that are emotionally aligned with individuals in order to help with prompt adherence and with long-term adoption.

Our speech research focuses on the detection of dialectal Most physiological assessments commonly used to determine variations by identifying speech language divergences the functional status of patients with Amyotrophic along a range of different dimensions. We borrow the notion lateral sclerosis (ALS) require trained clinical personnel to of divergence from the study of cross-linguistic variations administer and interpret the results. Speech impairments (Dorr, 1993) and apply it towards developing an assessment eventually affect 80-95% of patients with ALS (Beukelman, of bulbar function in patients with ALS, to improve 2011). Initial impairments include reduced speaking upon existing assessments (Green et al., 2013).

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.

Hutter's universal artificial intelligence (AI) showed how to define future AI systems by mathematical equations. Here we adapt those equations to define a self-modeling framework, where AI systems learn models of their own calculations of future values. Hutter discussed the possibility that AI agents may maximize rewards by corrupting the source of rewards in the environment. Here we propose a way to avoid such corruption in the self-modeling framework. This paper fits in the context of my book Ethical Artificial Intelligence.

Cyber-Physical Production Systems’ (CPPSs) main feature is adaptability, i.e. they can adapt quickly to new production goals such as new products or product variants. Today, the bottleneck of such approaches is the automation system, which still requires high manual engineering efforts for every adaptation step. Many recent solutions for a more adaptable automation software have focused on the automatic orchestration of software systems: for a new product and production configuration, a software solutions is created by putting together reusable software components. But such solutions come with a price: reusable software components must be, by definition, applicable to wide range of configurations. For this, software components come with free parameters that must be set according to the current configuration. Typically, the main problem is not the orchestration of software components but their correct parameterization. This paper presents, to the best of our knowledge for the first time, a solution to the parameterization problem of adaptable, CPPS-enable software systems. Due to the nature of CPPSs, no direct computation of parameters is possible. Instead, an iteration-based approach using a model of both the plant and the automation system is needed. An example from process industry illustrates the ideas.

Buildings make a city’s landscape and are home to its people. The demand for smart buildings and housing is growing by the need for cities to make their buildings more efficient, green and livable. This emergent intelligence is underpinned by the use of Information and Communications Technology (ICT) linked by Pervasive Sensing and real-time data analytics. In a typical growth of smart buildings, Smart Campuses are going to be amazing community hubs which will be more sustainable, efficient and supportive of its inhabitants. In this regard, huge amount of useful and real-time generated data are being analyzed to help people and machines infer instant decisions in relation to energy efficiency. However, because of different terminologies used by different players, structural, representational and semantic heterogeneity constrain the interoperability between applications and misleads to adaptive and context-aware control behavior. In this paper, the focus is to alleviate the current problem by designing a semantic framework that represents the smart campus data and activities in an ontological model. Also, the framework is deepened by an Artificial Intelligent (AI) method using Weighted Case Based Reasoning (WCBR) for enabling context awareness. An illustration will be the elaboration of an adaptive and autonomous control of HVAC (Heating Ventilation and Air Conditioning) system, in this example the WCBR is discussed and case representation, case adaptation, and similarity computation are sketched in detail.

In this challenge paper, we consider the importance of preferences in smart homes and assistive environments and discuss the potential application of models and algorithms developed within the computational preferences community. We suggest the value of future research collaborations.

Communication involves transferring information from one agent to another. An intelligent agent, either human or machine, is often able to choose to hide information in order to protect their interests. The notion of information hiding is closely linked to secrecy and dishonesty, but it also plays an important role in domains such as software engineering. In this paper, we consider the ethics of information hiding, particularly with respect to intelligent agents. In other words, we are concerned with situations that involve a human and an intelligent agent with access to different information. Is the intelligent agent justified in preventing a human user from accessing the information that they possess? This is trivially true in the case where access control systems exist. However, we are concerned with the situation where an intelligent agent is able to using a reasoning system to decide not to share information with all humans. On the other hand, we are also concerned with situations where humans hide information from machines. Are we ever under a moral obligation to share information with a computional agent? We argue that questions of this form are increasingly important now, as people are increasingly willing to divulge private information to machines with a great capacity to reason with that information and share it with others.

Under the auspices of “being green,” we have given our printed word over to a cyber-medium that cannot be touched or felt or folded. Our information is as volatile as the authority protecting our storage devices. Eliminating a book or changing its text can be done by literally pressing a button -- without a fire or an erasure marking, without smoke, without evidence. Our data is ephemeral along with the web in which we weave it. This paper considers the current ease of censorship, and that the non-permanence of data and links can wreak havoc on our societal infra-structure if the wrong entities (human or machine) with the wrong motives have the control to determine its fate.