Why Computers Can’t Feel Pain

The most cursory examination of the history of artificial intelligence highlights numerous egregious claims of its researchers, especially in relation to a populist form of ‘strong’ computationalism which holds that any suitably programmed computer instantiates genuine conscious mental states purely in virtue of carrying out a specific series of computations. The argument presented herein is a simple development of that originally presented in Putnam’s (Representation & Reality, Bradford Books, Cambridge in 1988) monograph, “Representation & Reality”, which if correct, has important implications for turing machine functionalism and the prospect of ‘conscious’ machines. In the paper, instead of seeking to develop Putnam’s claim that, “everything implements every finite state automata”, I will try to establish the weaker result that, “everything implements the specific machine Q on a particular input set (x)”. Then, equating Q (x) to any putative AI program, I will show that conceding the ‘strong AI’ thesis for Q (crediting it with mental states and consciousness) opens the door to a vicious form of panpsychism whereby all open systems, (e.g. grass, rocks etc.), must instantiate conscious experience and hence that disembodied minds lurk everywhere.     

Decision Making and Management of Dynamic Risk

Managing dynamic environments often requires decision making under uncertainty and risk. Two types of uncertainty are involved: uncertainty about the state and the evolution of the situation, and ‘openness’ of the possible actions to face possible consequences. In an experimental study on risk management in dynamic situations, two contrasted ‘ecological’ scenarios – transposed from effective situations of emergency management – were compared in order to identify the impact of their ‘openness’ in the subjects’ strategies for decision making. The ‘Lost Child’ scenario presented qualitative and irreversible consequences (child’s death) and high uncertainty; it exerted high demands both in risk assessment (risk representation) and action elaboration and choice. A less open situation (‘Hydrocarbon Fire’) required a main choice between two contrasted actions, with quantitative computable consequences. The strategies of ‘experimental subjects’ (university students) and ‘operative subjects’ (professional fire-fighter officers) were compared in order to evaluate the ecological validity of experimental research in this field, from the point of view of the subjects themselves. The two scenarios appeared to be independent, so that quite different models of decision making have to be hypothesised, differing by the importance of assessing risk and defining possible actions on the one hand, and by the process of choice on the other. ‘Experimental’ subjects dramatically differed from ‘operative’ subjects when confronted with the same scenario, particularly for the less technical but more demanding scenario. It is hypothesised that three components might account for the effect of the situations and for the differences between and within groups of subjects: importance of situation assessment, spatial abilities, and global orientation of activity in managing dynamic risk.     

Scenario Development and Practical Decision Making under Uncertainty: Application to Requirements Engineering

In this paper, we address the question of how flesh and blood decision makers manage the combinatorial explosion in scenario development for decision making under uncertainty. The first assumption is that the decision makers try to undertake ‘robust’ actions. For the decision maker a robust action is an action that has sufficiently good results whatever the events are. We examine the psychological as well as the theoretical problems raised by the notion of robustness. Finally, we address the false feeling of decision makers who talk of ‘risk control’. We argue that ‘risk control’ results from the thinking that one can postpone action after nature moves. This ‘action postponement’ amounts to changing look-ahead reasoning into diagnosis. We illustrate these ideas in the framework of software development and examine some possible implications for requirements analysis.     

YUTPA as a design tool for public participation

Engaging the public in decision-making processes is commonly accepted as an effective strategy for a better policy making, a better policy support and for narrowing the gap between government and the public. In today’s digitised society, participation via online media is becoming more important. But is this so-called e-participation being used optimally? Or is a better design possible? In my opinion, the answer to these questions is a ‘yes’. Despite numerous efforts in engaging the public with policy deliberation, the actual amount of participants remains low. In this article, I have used the YUTPA model (Nevejan 2009) to analyse some existing e-participation projects. Additionally, I derived ten characteristics of ‘play’ to make proposals for a more designerly e-participation approach.     

Learning to search: Functional gradient techniques for imitation learning

Programming robot behavior remains a challenging task. While it is often easy to abstractly define or even demonstrate a desired behavior, designing a controller that embodies the same behavior is difficult, time consuming, and ultimately expensive. The machine learning paradigm offers the promise of enabling “programming by demonstration” for developing high-performance robotic systems. Unfortunately, many “behavioral cloning” (Bain and Sammut in Machine intelligence agents. London: Oxford University Press, 1995; Pomerleau in Advances in neural information processing systems 1, 1989; LeCun et al. in Advances in neural information processing systems 18, 2006) approaches that utilize classical tools of supervised learning (e.g. decision trees, neural networks, or support vector machines) do not fit the needs of modern robotic systems. These systems are often built atop sophisticated planning algorithms that efficiently reason far into the future; consequently, ignoring these planning algorithms in lieu of a supervised learning approach often leads to myopic and poor-quality robot performance. While planning algorithms have shown success in many real-world applications ranging from legged locomotion (Chestnutt et al. in Proceedings of the IEEE-RAS international conference on humanoid robots, 2003) to outdoor unstructured navigation (Kelly et al. in Proceedings of the international symposium on experimental robotics (ISER), 2004; Stentz et al. in AUVSI’s unmanned systems, 2007), such algorithms rely on fully specified cost functions that map sensor readings and environment models to quantifiable costs. Such cost functions are usually manually designed and programmed. Recently, a set of techniques has been developed that explore learning these functions from expert human demonstration. These algorithms apply an inverse optimal control approach to find a cost function for which planned behavior mimics an expert’s demonstration. The work we present extends the Maximum Margin Planning (MMP) (Ratliff et al. in Twenty second international conference on machine learning (ICML06), 2006a) framework to admit learning of more powerful, non-linear cost functions. These algorithms, known collectively as LEARCH (LEArning to seaRCH), are simpler to implement than most existing methods, more efficient than previous attempts at non-linearization (Ratliff et al. in NIPS, 2006b), more naturally satisfy common constraints on the cost function, and better represent our prior beliefs about the function’s form. We derive and discuss the framework both mathematically and intuitively, and demonstrate practical real-world performance with three applied case-studies including legged locomotion, grasp planning, and autonomous outdoor unstructured navigation. The latter study includes hundreds of kilometers of autonomous traversal through complex natural environments. These case-studies address key challenges in applying the algorithm in practical settings that utilize state-of-the-art planners, and which may be constrained by efficiency requirements and imperfect expert demonstration.

Toward objective software process information: experiences from a case study

A critical problem in software development is the monitoring, control and improvement in the processes of software developers. Software processes are often not explicitly modeled, and manuals to support the development work contain abstract guidelines and procedures. Consequently, there are huge differences between ‘actual’ and ‘official’ processes: “the actual process is what you do, with all its omissions, mistakes, and oversights. The official process is what the book, i.e., a quality manual, says you are supposed to do” (Humphrey in A discipline for software engineering. Addison-Wesley, New York, 1995). Software developers lack support to identify, analyze and better understand their processes. Consequently, process improvements are often not based on an in-depth understanding of the ‘actual’ processes, but on organization-wide improvement programs or ad hoc initiatives of individual developers. In this paper, we show that, based on particular data from software development projects, the underlying software development processes can be extracted and that automatically more realistic process models can be constructed. This is called software process mining (Rubin et al. in Process mining framework for software processes. Software process dynamics and agility. Springer Berlin, Heidelberg, 2007). The goal of process mining is to better understand the development processes, to compare constructed process models with the ‘official’ guidelines and procedures in quality manuals and, subsequently, to improve development processes. This paper reports on process mining case studies in a large industrial company in The Netherlands. The subject of the process mining is a particular process: the change control board (CCB) process. The results of process mining are fed back to practice in order to subsequently improve the CCB process.     

A CORBA-based agent-driven design for distributed intelligent manufacturing systems

An agent-oriented methodology is presented for representation, acquisition, and processing of manufacturing knowledge along with analysis and modeling of an intelligent manufacturing system (IMS). An intelligent manufacturing system adopts heterarchical and collaborative control as its information system architecture. The behavior of the entire manufacturing system is collaboratively determined by many interacting subsystems that may have their own independent interests, values, and modes of operation. The subsystems are represented as agents. An agent's architecture and task decomposition method are presented. The agent-oriented methodology is used to analyze and model an intelligent machine cell. An intelligent machine center is considered as an autonomous, modular, reconfigurable and fault-tolerant machine tool with self-perception, decision making, and self-process planning, able to cooperate with other machines through communication. The common object request broker architecture (CORBA) distributed software control system was developed as a simple prototype. A case study illustrates an intelligent machine center.     

A Corrigendum to: “Sticky-ERfair: a task-processor affinity aware proportional fair scheduler”

This paper provides a corrigendum to resolve a couple of minor issues in the algorithm presented in Sarkar et al. (Real-Time Syst., 2011). The first issue relates to the postponement of execution of a task when its own ‘deadline of postponement’ have not been crossed. The second issue concerns the updation of certain scheduler data structures.     

The city is connections: Seoul as an urban network

With the rise of ubiquitous computing in recent years, concepts of spatiality have become a significant topic of discussion in design and development of multimedia systems. This article investigates spatial practices at the intersection of youth, technology, and urban space in Seoul, and examines what the author calls ‘transyouth’: in the South Korean context, these people are between the ages of 18 and 24, situated on the delicate border between digital natives and immigrants in Prensky’s [46] terms. In the first section, the article sets out the technosocial environment of contemporary Seoul. This is followed by a discussion of social networking processes derived from semi-structured interviews conducted in 2007–2008 with Seoul transyouth about their ‘lived experiences of the city.’ Interviewees reported how they interact to play, work, and live with and within the city’s unique environment. The article develops a theme of how technosocial convergence (re)creates urban environments and argues for a need to consider such user-driven spatial recreation in designing cities as (ubiquitous) urban networks in recognition of its changing technosocial contours of connections. This is explored in three spaces of different scales: Cyworld as an online social networking space; cocoon housing—a form of individual residential space which is growing rapidly in many Korean cities—as a private living space; and ubiquitous City as the future macro-space of Seoul.     

Socio-ethics of interaction with intelligent interactive technologies

Socio-ethics covers the relation of the individual with the group and with society, as the individual acquires the skills for social life with others and the conduct of ‘normal responsible behaviour’ (Leal in AI Soc 9:29–32, 1995) that guides moral action. For a consideration of what it means to be socially skilled in everyday human interaction and the ethical issues arising from the new conditions of interaction that come with the integration of intelligent interactive artefacts, we will provide an analysis at multiple levels of these phenomena and draw on a variety of application domains, for example, healthcare and interactive media.     

Mobility characterization for autonomous mobile robots using machine learning

This paper presents a supervised learning approach to improving the autonomous mobility of wheeled robots through sensing the robot’s interaction with terrain ‘underfoot.’ Mobility characterization is cast as a hierarchical task, in which pre-immobilization detection is achieved using support vector machines in time to prevent full immobilization, and if a pre-immobilization condition is detected, the associated terrain feature affecting mobility is identified using a Hidden Markov model. These methods are implemented using a hierarchical, layered control scheme developed for the Yeti robot, a 73-kg, four-wheeled robot designed to perform autonomous medium-range missions in polar terrain. The methodology is motivated by the difficultly of visually recognizing terrain features that impact mobility in low contrast terrain. The efficacy of the approach is evaluated using data from a suite of proprioceptive sensors. Real-time implementation shows that Yeti can consistently detect pre-immobilization conditions, stop in time to avoid unrecoverable immobilization, identify the terrain feature presenting the mobility challenge, and execute an escape sequence to retreat from the condition.     

A note on group decision-making procedure based on incomplete reciprocal relations

In a very recent paper by Xu and Chen (Soft Comput 12:515–521, 2008), a novel procedure for group decision making with incomplete reciprocal relations was developed. In this note, we examine the function between the fuzzy preference relation and its corresponding priority vector developed by Xu and Chen with a numerical example and show that the function does not hold in general cases. Then, we deduce an exact function between the additive transitivity fuzzy preference relation and its corresponding priority vector. Based on this, we develop a procedure for the decision making with an incomplete reciprocal relation and also develop a procedure for the group decision making with incomplete reciprocal relations. In order to compare the performances of our method with Xu and Chen’s method in fitting the reciprocal relation, we introduce some criteria. Theoretical analysis and numerical examples have shown that the function deduced by us is more reasonable and effective than Xu and Chen’s.     

Cognitive and synthetic behavior of avatars in intelligent virtual environments

In intelligent virtual environments (IVEs), it is a challenging research issue to provide the intelligent virtual actors (or avatars) with the ability of visual perception and rapid response to virtual world events. Modeling an avatar’s cognitive and synthetic behavior appropriately is of paramount important in IVEs. We propose a new cognitive and behavior modeling methodology that integrates two previously developed complementary approaches. We present expression cloning, walking synthetic behavior modeling, and an autonomous agent cognitive model for driving an avatar’s behavior. Facial expressions are generated using our own-developed rule-based state transition system. Facial expressions are further personalized for individuals by expression cloning. An avatar’s walking behavior is modeled using a skeleton model that is implemented by seven-motion sequences and finite state machines (FSMs). We discuss experimental results demonstrating the benefits of our approach.     

The effects of scent and game play experience on memory of a virtual environment

Scent has been well documented as having significant effects on emotion (Alaoui-Ismaili in Physiol Behav 62(4):713–720, 1997; Herz et al. in Motiv Emot 28(4):363–383, 2004), learning (Smith et al. in Percept Mot Skills 74(2):339–343, 1992; Morgan in Percept Mot Skills 83(3)(2):1227–1234, 1996), memory (Herz in Am J Psychol 110(4):489–505, 1997) and task performance (Barker et al. in Percept Mot Skills 97(3)(1):1007–1010, 2003). This paper describes an experiment in which environmentally appropriate scent was presented as an additional sensory modality consistent with other aspects of a virtual environment called DarkCon. Subjects’ game play habits were recorded as an additional factor for analysis. Subjects were randomly assigned to receive scent during the VE, and/or afterward during a task of recall of the environment. It was hypothesized that scent presentation during the VE would significantly improve recall, and that subjects who were presented with scent during the recall task, in addition to experiencing the scented VE, would perform the best on the recall task. Skin-conductance was a significant predictor of recall, over and above experimental groups. Finally, it was hypothesized that subjects’ game play habits would affect both their behavior in and recall of the environment. Results are encouraging to the use of scent in virtual environments, and directions for future research are discussed. The project described herein has been sponsored by the US Army Research, Development, and Engineering Command (RDECOM). Statements and opinions expressed do not necessarily reflect the position or the policy of the US Government; no official endorsement should be inferred.     

CaRINA Intelligent Robotic Car: Architectural design and applications

This paper presents the development of two outdoor intelligent vehicles platforms named CaRINA I and CaRINA II, their system architecture, simulation tools, and control modules. It also describes the development of the intelligent control system modules allowing the mobile robots and vehicles to navigate autonomously in controlled urban environments. Research work has been carried out on tele-operation, driver assistance systems, and autonomous navigation using the vehicles as platforms to experiments and validation. Our robotic platforms include mechanical adaptations and the development of an embedded software architecture. This paper addresses the design, sensing, decision making, and acting infrastructure and several experimental tests that have been carried out to evaluate both platforms and proposed algorithms. The main contributions of this work is the proposed architecture, that is modular and flexible, allowing it to be instantiated into different robotic platforms and applications. The communication and security aspects are also investigated.     

The ontological status of computers or what is a computer?

The development of computers as ‘mind tools’ has generated intriguing and provocative views about their potential human-like qualities. In this paper an attempt is made to explore the ‘real’ nature of computers by an examination of three widely different perspective, (1) the common-sense view of computers as tools; (2) the provocative view of computers as persons; and (3) the challenging view of computers as texts. In the course of the discussion an extended critique of the use of anthropomorphic terms in relation to machines is conducted and the fundamental differences between human persons and machines is reasserted.     

An Automatic Correction of Ma’s Thinning Algorithm Based on <Emphasis Type="Italic">P</Emphasis>-simple Points

The notion of P-simple points was introduced by Bertrand to conceive parallel thinning algorithms. In ‘A 3D fully parallel thinning algorithm for generating medial faces’ (Pattern Recogn. Lett. 16:83–87, 1995), Ma proposed an algorithm for which there are objects whose topology is not preserved. In this paper, we propose a new application of P-simple points: to automatically correct Ma’s algorithm.     

GridSAT: Design and Implementation of a Computational Grid Application

We present the latest instantiation of GridSAT [1], a distributed and complete satisfiability solver that is explicitly designed to aggregate Grid resources for application performance. GridSAT was previously shown to outperform the state-of-the-art sequential solvers. In this work, we explore the unprecedented solving power GridSAT enables through algorithmic and implementation innovations. We describe the implementation techniques that allow GridSAT to leverage a variety of high-end batch-scheduled resources, clusters, interactive workstations, and personal computing resources through autonomous scheduling, checkpoint scheduling, and work migration. These innovations have allowed GridSAT to solve a set of ‘hard’ and previously unsolved industrial and community satisfiability problems. In addition to this new solution power, GridSAT also outperforms the otherwise highest performance general solvers on the annual SAT competition [21] performance benchmarks.This work was supported by grants from the National Science Foundation, numbered CAREER-0093166, EIA-9975020, ACI-0103759, and CCR-0331654 and by the San Diego Supercomputer Center and the TeraGrid project.     

Numerical simulation of the motion of red blood cells and vesicles in microfluidic flows

We study the mathematical modeling and numerical simulation of the motion of red blood cells (RBC) and vesicles subject to an external incompressible flow in a microchannel. RBC and vesicles are viscoelastic bodies consisting of a deformable elastic membrane enclosing an incompressible fluid. We provide an extension of the finite element immersed boundary method by Boffi and Gastaldi (Comput Struct 81:491–501, 2003), Boffi et al. (Math Mod Meth Appl Sci 17:1479–1505, 2007), Boffi et al. (Comput Struct 85:775–783, 2007) based on a model for the membrane that additionally accounts for bending energy and also consider inflow/outflow conditions for the external fluid flow. The stability analysis requires both the approximation of the membrane by cubic splines (instead of linear splines without bending energy) and an upper bound on the inflow velocity. In the fully discrete case, the resulting CFL-type condition on the time step size is also more restrictive. We perform numerical simulations for various scenarios including the tank treading motion of vesicles in microchannels, the behavior of ‘healthy’ and ‘sick’ RBC which differ by their stiffness, and the motion of RBC through thin capillaries. The simulation results are in very good agreement with experimentally available data.