Infants at risk for cerebral palsy are at a severe disadvantage in learning to crawl as compared with typically developing infants. An assistive system is being created at the University of Oklahoma to improve these children's crawling abilities. The infants are: outfitted with a suit that allows kinematic reconstruction of their movements; EEG monitoring of their neural responses; and placed in an assistive robot that can amplify the effectiveness of their crawling actions and reduce the required weight bearing for successful prone locomotion. The system can also map their attempted motions into a library of recognized movements, and create directed robot motion even when the subject has not generated any propulsive forces on their own.

Cochlear implants (CIs) are an effective intervention for individuals with severe-to-profound sensorineural hearing loss. Currently, no tuning procedure exists that can fully exploit the technology. We propose online unsupervised algorithms to learn features from the speech of a severely-to-profoundly hearing-impaired patient round-the-clock and compare the features to those learned from the normal hearing population using a set of neurophysiological metrics. Experimental results are presented. The information from comparison can be exploited to modify the signal processing in a patient’s CI to enhance his audibility of speech.

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.

Relentless progress in artificial intelligence (AI) is increasingly raising concerns that machines will replace humans on the job market, and perhaps altogether. Eliezer Yudkowski and others have explored the possibility that a promising future for humankind could be guaranteed by a superintelligent "Friendly AI" , designed to safeguard humanity and its values. I will argue that, from a physics perspective where everything is simply an arrangement of elementary particles, this might be even harder than it appears. Indeed, it may require thinking rigorously about the meaning of life: What is "meaning" in a particle arrangement? What is "life"? What is the ultimate ethical imperative, i.e., how should we strive to rearrange the particles of our Universe and shape its future? If we fail to answer the last question rigorously, this future is unlikely to contain humans.

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.

A paradigm of case-supported principle-based behavior (CPB) is proposed to help ensure ethical behavior of autonomous machines. We argue that ethically significant behavior of autonomous systems should be guided by explicit ethical principles determined through a consensus of ethicists. Such a consensus is likely to emerge in many areas in which autonomous systems are apt to be deployed and for the actions they are liable to undertake, as we are more likely to agree on how machines ought to treat us than on how human beings ought to treat one another. Given such a consensus, particular cases of ethical dilemmas where ethicists agree on the ethically relevant features and the right course of action can be used to help discover principles needed for ethical guidance of the behavior of autonomous systems. Such principles help ensure the ethical behavior of complex and dynamic systems and further serve as a basis for justification of their actions as well as a control abstraction for managing unanticipated behavior. The requirements, methods, implementation, and evaluation components of the CPB paradigm are detailed.

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.

To achieve high performance, autonomous systems, such as science explorers, should adapt to the environment to improve utility gained, as well as robustness. Flexibility during temporal plan execution has been explored extensively to improve robustness, where flexibility exists both in activity choices and schedules. These problems are framed as conditional constraint networks over temporal constraints. However, flexibility has been exploited in a limited form to improve utility. Prior work considers utility in choice or schedule, but not their coupling. To exploit fully flexibility, we introduce conditional simple temporal networks with preference (CSTNP), where preference is a function over both choice and schedule. Enumerating best solutions to a CSTNP is challenging due to the cost of scheduling a candidate STPP and the exponential number of candidates. Our contribution is an algorithm for enumerating solutions to CSTNPs efficiently, called A star with bounding conflicts (A*BC), and a novel variant of conflicts, called bounding conflicts, for learning heuristic functions. A*BC interleaves Generate, Test, and Bound. When A*BC bounds a candidate, by solving a STPP, it generates a bounding conflict, denoting neighboring candidates with similar bounds. A*BC's generator then uses these conflicts to steer away from sub-optimal candidates.

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).

We examine a decomposition approach to find good quality feasible solutions. In particular, we studya method to reduce the search-space by decomposing a problem into two partitions, where the second partition (i.e., the subproblem) contains the fixed solution of the first (i.e., the master). This type of approach is usually motivated by the presence of two sub-problems that are each more easily solved by different methods. Our work is motivated by methods for which it is nontrivial to return a strong `no-good', `Benders feasibility', or 'optimality' cut. Instead, we focus our attention on a uni-directional decomposition approach. Instead of providing a relaxation of the sub-problem for the master problem, as in Benders decomposition, we provide an approximation of the sub-problem. Thus, we aim at finding good quality feasible solutions in the first iteration. While the quality of the approximation itself affects the impact of this approach, we illustrate that even using a simple approximation can havestrong positive impact on two examples: the Travelling Purchaser Problem and a Mine Planning Problem.