A Boltzmann machine for the organization of intelligent machines

In the present technological society, there is a major need to build machines that would execute intelligent tasks operating in uncertain environments with minimum interaction with a human operator. Although some designers have built smart robots, utilizing heuristic ideas, there is no systematic approach to design such machines in an engineering manner. Recently, cross-disciplinary research from the fields of computers, systems AI and information theory has served to set the foundations of the emerging area of the design of intelligent machines. Since 1977 Saridis has been developing an approach, defined as Hierarchical Intelligent Control, designed to organize, coordinate and execute anthropomorphic tasks by a machine with minimum interaction with a human operator. This approach utilizes analytical (probabilistic) models to describe and control the various functions of the intelligent machine structured by the intuitively defined principle of Increasing Precision with Decreasing Intelligence (IPDI) (Saridis 1979). This principle, even though resembles the managerial structure of organizational systems (Levis 1988), has been derived on an analytic basis by Saridis (1988). The purpose is to derive analytically a Boltzmann machine suitable for optimal connection of nodes in a neural net (Fahlman, Hinton, Sejnowski, 1985). Then this machine will serve to search for the optimal design of the organization level of an intelligent machine. In order to accomplish this, some mathematical theory of the intelligent machines will be first outlined. Then some definitions of the variables associated with the principle, like machine intelligence, machine knowledge, and precision will be made (Saridis, Valavanis 1988). Then a procedure to establish the Boltzmann machine on an analytic basis will be presented and illustrated by an example in designing the organization level of an Intelligent Machine. A new search technique, the Modified Genetic Algorithm, is presented and proved to converge to the minimum of a cost function. Finally, simulations will show the effectiveness of a variety of search techniques for the intelligent machine

Creating Interaction Scenarios With a New Graphical User Interface

The field of human-centered computing has known a major progress these past few years. It is admitted that this field is multidisciplinary and that the human is the core of the system. It shows two matters of concern: multidisciplinary and human. The first one reveals that each discipline plays an important role in the global research and that the collaboration between everyone is needed. The second one explains that a growing number of researches aims at making the human commitment degree increase by giving him/her a decisive role in the human-machine interaction. This paper focuses on these both concerns and presents MICE (Machines Interaction Control in their Environment) which is a system where the human is the one who makes the decisions to manage the interaction with the machines. In an ambient context, the human can decide of objects actions by creating interaction scenarios with a new visual programming language: scenL.Comment: 5th International Workshop on Intelligent Interfaces for Human-Computer Interaction, Palerme : Italy (2012

PIWeCS: enhancing human/machine agency in an interactive composition system

This paper focuses on the infrastructure and aesthetic approach used in PIWeCS: a Public Space Interactive Web-based Composition System. The concern was to increase the sense of dialogue between human and machine agency in an interactive work by adapting Paine's (2002) notion of a conversational model of interaction as a ‘complex system’. The machine implementation of PIWeCS is achieved through integrating intelligent agent programming with MAX/MSP. Human input is through a web infrastructure. The conversation is initiated and continued by participants through arrangements and composition based on short performed samples of traditional New Zealand Maori instruments. The system allows the extension of a composition through the electroacoustic manipulation of the source material

Following wrong suggestions: self-blame in human and computer scenarios

This paper investigates the specific experience of following a suggestion by an intelligent machine that has a wrong outcome and the emotions people feel. By adopting a typical task employed in studies on decision-making, we presented participants with two scenarios in which they follow a suggestion and have a wrong outcome by either an expert human being or an intelligent machine. We found a significant decrease in the perceived responsibility on the wrong choice when the machine offers the suggestion. At present, few studies have investigated the negative emotions that could arise from a bad outcome after following the suggestion given by an intelligent system, and how to cope with the potential distrust that could affect the long-term use of the system and the cooperation. This preliminary research has implications in the study of cooperation and decision making with intelligent machines. Further research may address how to offer the suggestion in order to better cope with user's self-blame.Comment: To be published in the Proceedings of IFIP Conference on Human-Computer Interaction (INTERACT)201

Metal oxide semiconductor nanomembrane-based soft unnoticeable multifunctional electronics for wearable human-machine interfaces

Wearable human-machine interfaces (HMIs) are an important class of devices that enable human and machine interaction and teaming. Recent advances in electronics, materials, and mechanical designs have offered avenues toward wearable HMI devices. However, existing wearable HMI devices are uncomfortable to use and restrict the human body's motion, show slow response times, or are challenging to realize with multiple functions. Here, we report sol-gel-on-polymer-processed indium zinc oxide semiconductor nanomembrane-based ultrathin stretchable electronics with advantages of multifunctionality, simple manufacturing, imperceptible wearing, and robust interfacing. Multifunctional wearable HMI devices range from resistive random-access memory for data storage to field-effect transistors for interfacing and switching circuits, to various sensors for health and body motion sensing, and to microheaters for temperature delivery. The HMI devices can be not only seamlessly worn by humans but also implemented as prosthetic skin for robotics, which offer intelligent feedback, resulting in a closed-loop HMI system

Intelligent Interactive Multimedia by Converging the Intention of Spectator and Multimedia Creator

In this research, we propose a new approach on how human and technology interact with each other. Here, by enhancing the current HCI framework, it will enable interaction between human and technology become more effective and ideally. The aim of this research is to create an Intelligent Interactive Multimedia by converging the intention of spectator and multimedia creator. Several methods are proposed to achieve the conception of Intelligent Interactive Multimedia. Digital Drawing Block is the interactive multimedia with the initial intention of multimedia creator and it forms an interaction with spectator. Spectator intention has been categorized into four common categories, additionally, five features of hand gesture recognition is proposed to deduce the spectator intention. All these five features will be captured by the web-cam during the spectator’s interaction with the Digital Drawing Block. Moreover, captured features will be sent to the machine learning for analyzing. Proposed user models are to assist the machine learning to evaluate the most appropriate category of human behaviour which matches the spectator actual intention. Lastly, graphic that represents spectator intention will be generated together with the initial intention of multimedia creator. The new creation from spectator and multimedia creator will be displayed through the Digital Drawing Block. The conception of Intelligent Interactive Multimedia can represent as 70%'s effort of Multimedia Creator + 30%'s effort of spectator

Human adaptation to adaptive machines converges to game-theoretic equilibria

Adaptive machines have the potential to assist or interfere with human behavior in a range of contexts, from cognitive decision-making to physical device assistance. Therefore it is critical to understand how machine learning algorithms can influence human actions, particularly in situations where machine goals are misaligned with those of people. Since humans continually adapt to their environment using a combination of explicit and implicit strategies, when the environment contains an adaptive machine, the human and machine play a game. Game theory is an established framework for modeling interactions between two or more decision-makers that has been applied extensively in economic markets and machine algorithms. However, existing approaches make assumptions about, rather than empirically test, how adaptation by individual humans is affected by interaction with an adaptive machine. Here we tested learning algorithms for machines playing general-sum games with human subjects. Our algorithms enable the machine to select the outcome of the co-adaptive interaction from a constellation of game-theoretic equilibria in action and policy spaces. Importantly, the machine learning algorithms work directly from observations of human actions without solving an inverse problem to estimate the human's utility function as in prior work. Surprisingly, one algorithm can steer the human-machine interaction to the machine's optimum, effectively controlling the human's actions even while the human responds optimally to their perceived cost landscape. Our results show that game theory can be used to predict and design outcomes of co-adaptive interactions between intelligent humans and machines

Sustainable agriculture using an intellingent mechatronic system

The goal of the Project group created by U.P.M. in collaboration with Foreign Universities, Research Institutions and Companies is the development of an intelligent mechatronic system for the use of precision and sustainable agriculture. The project as a whole includes the following components: photographing and decoding of the soil surface; fertility determination and formation of the fertility map; generation of the controlling signal for mechatronic dosing device; intelligent dosing of fertilizers; simulation, prototype and testing; human-machine interaction and training preparation