Much AI research on computational sustainability has focused on monitoring, modeling, analysis, and optimization of existing systems and processes. In this article, we present another exciting and promising paradigm for AI research on computational sustainability that emphasizes design of new systems and processes, and, in particular, on biologically inspired design. We first characterize biologically inspired design, then examine its relationship with environmental sustainability, next present a computational model of the process of biologically inspired design, and finally describe a few computational systems for supporting biologically inspired design practice.

Past research has studied two approaches to utilise pre-defined policy sets in repeated interactions: as experts, to dictate our own actions, and as types, to characterise the behaviour of other agents. In this work, we bring these complementary views together in the form of a novel meta-algorithm, called Expert-HBA (E-HBA), which can be applied to any expert algorithm that considers the average (or total) payoff an expert has yielded in the past. E-HBA gradually mixes the past payoff with a predicted future payoff, which is computed using the type-based characterisation. We present results from a comprehensive set of repeated matrix games, comparing the performance of several well-known expert algorithms with and without the aid of E-HBA. Our results show that E-HBA has the potential to significantly improve the performance of expert algorithms.

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.

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.

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.

In the presented research project, a software and hardware infrastructure for parking space focussed inter-modal route planning in a public pilot region in Berlin is developed. One central topic is the development of a prediction system which gives an estimated occupancy for the parking spaces in the pilot region for a given date and time in the future. Occupancy data will be collected online by roadside parking sensors developed within the project. The occupancy prediction will be implemented using “Neural Gas” machine learning in combination with a proposed method which uses data threads to improve the prediction quality. In this paper, a short overview of the whole research project is given. Furthermore, the concept of the software framework and the learning methods are presented and first collected data is shown. The prediction method using data threads is explained in more detail.

Algorithm selection (AS) techniques — which involve choosing from a set of algorithms the one expected to solve a given problem instance most efficiently — have substantially improved the state-of-the-art in solving many prominent AI problems, such as SAT, CSP, ASP, MAXSAT, and QBF.Although several AS procedures have been introduced,not too surprisingly, none of them dominates all others across all AS scenarios.Furthermore, these procedures have parameters whose optimal values vary across AS scenarios.This holds specifically for the machine learning techniques that form the core of current AS proceduresand for their hyperparameters. Therefore, to successfully apply AS to new problems, algorithms and benchmark sets, two questions need to be answered:(i) how to select an AS approach and (ii) how to set its parameters effectively.We address both of these problems simultaneously by using automated algorithm configuration.Specifically, we demonstrate that we can use algorithm configurators to automatically configure clasp folio 2,which implements a large variety of different AS approaches and their respective parameters in a single highly parameterized algorithm framework.We demonstrate that this approach, dubbed auto folio, can significantly improve the performance of clasp folio 2 on 11 out of the 12 scenarios from the Algorithm Selection Library and leads to new state-of-the-art algorithm selectors for 8 of these scenarios.

The number of elderly and frail individuals in need of daily assistance increases and the available human resources will certainly be insufficient. To remedy this situation, smart habitats are considered by many researchers as an innovative avenue to help support the needs of elders. It aims at providing cognitive assistance in taking decisions by giving hints, suggestions, and reminders with different kinds of effectors to residents. To implement such technology, the first challenge we need to overcome is the recognition of the ongoing activity. In the literature, some researchers have proposed solutions based on cameras, binary sensors, radio-frequency identification and load signatures of appliances but all these types of approaches have certain limitations to perform a complete recognition. In order to provide additional and useful information, a complementary activity recognition system, based on environmental sounds and able to detect errors related to cognitive impairment, is presented in this paper. The entire system relies on a discrete wavelet transform, the zero-crossing rate and C4.5 algorithm. This system has been implemented and deployed in a real smart-home prototype. This paper also present the results of a first set of experiments conducted on this system with real cases scenarios.

As artificially intelligent systems grow in intelligence and capability, some of their available options may allow them to resist intervention by their programmers. We call an AI system "corrigible" if it cooperates with what its creators regard as a corrective intervention, despite default incentives for rational agents to resist attempts to shut them down or modify their preferences. We introduce the notion of corrigibility and analyze utility functions that attempt to make an agent shut down safely if a shutdown button is pressed, while avoiding incentives to prevent the button from being pressed or cause the button to be pressed, and while ensuring propagation of the shutdown behavior as it creates new subsystems or self-modifies. While some proposals are interesting, none have yet been demonstrated to satisfy all of our intuitive desiderata, leaving this simple problem in corrigibility wide-open.

We analyze the results of assistance to individuals with autism in reasoning about mental world. This assistance is provided by a natural language multiagent simulator of mental states, NL_MAMS (Galitsky 2013b). It assists in the tasks which are the hardest for autistic reasoning: operating with mental states and actions. Autistic patients are trained to perform a number of reasoning exercises. We conduct both short term and long term evaluations including the behavior in real world and confirm that the system has a positive effect on their rehabilitation.