The reuse of scientific knowledge obtained from one investigation in another investigation is basic to the advance of science. Scientific investigations should therefore be recorded in ways that promote the reuse of the knowledge they generate. The use of logical formalisms to describe scientific knowledge has potential advantages in facilitating such reuse. Here, we propose a formal framework for using logical formalisms to promote reuse. We demonstrate the utility of this framework by using it in a worked example from biology: demonstrating cycles of investigation formalization [F] and reuse [R] to generate new knowledge. We first used logic to formally describe a Robot scientist investigation into yeast (Saccharomyces cerevisiae) functional genomics [f1]. With Robot scientists, unlike human scientists, the production of comprehensive metadata about their investigations is a natural by-product of the way they work. We then demonstrated how this formalism enabled the reuse of the research in investigating yeast phenotypes [r1 = R(f1)]. This investigation found that the removal of non-essential enzymes generally resulted in enhanced growth. The phenotype investigation was then formally described using the same logical formalism as the functional genomics investigation [f2 = F(r1)]. We then demonstrated how this formalism enabled the reuse of the phenotype investigation to investigate yeast systems-biology modelling [r2 = R(f2)]. This investigation found that yeast flux-balance analysis models fail to predict the observed changes in growth. Finally, the systems biology investigation was formalized for reuse in future investigations [f3 = F(r2)]. These cycles of reuse are a model for the general reuse of scientific knowledge.
semantic web; logic; Saccharomyces cerevisiae; ontology
Hypotheses are now being automatically produced on an industrial scale by computers in biology, e.g. the annotation of a genome is essentially a large set of hypotheses generated by sequence similarity programs; and robot scientists enable the full automation of a scientific investigation, including generation and testing of research hypotheses.
This paper proposes a logically defined way for recording automatically generated hypotheses in machine amenable way. The proposed formalism allows the description of complete hypotheses sets as specified input and output for scientific investigations. The formalism supports the decomposition of research hypotheses into more specialised hypotheses if that is required by an application. Hypotheses are represented in an operational way – it is possible to design an experiment to test them. The explicit formal description of research hypotheses promotes the explicit formal description of the results and conclusions of an investigation. The paper also proposes a framework for automated hypotheses generation. We demonstrate how the key components of the proposed framework are implemented in the Robot Scientist “Adam”.
A formal representation of automatically generated research hypotheses can help to improve the way humans produce, record, and validate research hypotheses.
Successful management of laboratory robotic automation programmes
in the environment of research and drug discovery within
the pharmaceutical industry may perhaps be best compared to a chef
preparing the perfect hollandaise sauce. All the ingredients must be
available at the same time and be of highest quality for the right
price. However, if components are not added in the right quantities
and in the proper order, no amount of whipping together by the
product champion will create the best product. In the past,
managerial scepticism surrounding useful implementation of cost-effective,
high-throughput robotic systems often placed these
‘modern toys’ at low priorities for research development laboratories.
Management now recognizes the unique contributions of
robotics in the research environment. Although the scientific director
must still play the role of product champion, new questions are
being proposed and new commitments are being made to bring the
potential of robotic automation to every laboratory where repetitive
functions can benefit from new applications. Research laboratory
directors have become both the key ingredient, as well as the rate-limiting
determinant in the development of new applications.
Having fulfilled the promise of robotic automation to release
talented personnel, the challenge now is for the ‘end users’, the bench
scientists, to be provided with opportunities to invest the time and
effort required for future applications and new career functions.
I describe a number of valuable lessons I learned from participating in California's Proposition 71 effort about the role that scientists and rigorous scientific advice can play in a public political process. I describe how scientists can provide valuable information and advice and how they can also gain a great deal from the experience that is valuable to a practicing research scientist. Finally, I argue that in the future, building similar broad coalitions to support biomedical and other areas of scientific research will be essential to protect publicly funded science. Thus, a key lesson from the Proposition 71 experience is that engagement of scientists with diverse nonscientific groups can make a big difference and that scientists must actively engage with the public in the future if we are to contribute robustly to the medical and economic health of our communities.
Scientific discovery is incremental. The Merriam-Webster definition of 'Scientific Method' is "principles and procedures for the systematic pursuit of knowledge involving the recognition and formulation of a problem, the collection of data through observation and experiment, and the formulation and testing of hypotheses". Scientists are taught to be excellent observers, as observations create questions, which in turn generate hypotheses. After centuries of science we tend to assume that we have enough observations to drive science, and enable the small steps and giant leaps which lead to theories and subsequent testable hypotheses. One excellent example of this is Charles Darwin's Voyage of the Beagle, which was essentially an opportunistic survey of biodiversity. Today, obtaining funding for even small-scale surveys of life on Earth is difficult; but few argue the importance of the theory that was generated by Darwin from his observations made during this epic journey. However, these observations, even combined with the parallel work of Alfred Russell Wallace at around the same time have still not generated an indisputable 'law of biology'. The fact that evolution remains a 'theory', at least to the general public, suggests that surveys for new data need to be taken to a new level.
To bring cutting edge robotics from research centres to social environments, the robotics community must start providing affordable solutions: the costs must be reduced and the quality and usefulness of the robot services must be enhanced. Unfortunately, nowadays the deployment of robots and the adaptation of their services to new environments are tasks that usually require several days of expert work. With this in view, we present a multi-agent system made up of intelligent cameras and autonomous robots, which is easy and fast to deploy in different environments. The cameras will enhance the robot perceptions and allow them to react to situations that require their services. Additionally, the cameras will support the movement of the robots. This will enable our robots to navigate even when there are not maps available. The deployment of our system does not require expertise and can be done in a short period of time, since neither software nor hardware tuning is needed. Every system task is automatic, distributed and based on self-organization processes. Our system is scalable, robust, and flexible to the environment. We carried out several real world experiments, which show the good performance of our proposal.
robot deployment; robot detection and tracking; multi-camera networks; ambient intelligence; ubiquitous robots
Experiencing the flow of time is an important capacity of biological systems that is involved in many ways in the daily activities of humans and animals. However, in the field of robotics, the key role of time in cognition is not adequately considered in contemporary research, with artificial agents focusing mainly on the spatial extent of sensory information, almost always neglecting its temporal dimension. This fact significantly obstructs the development of high-level robotic cognitive skills, as well as the autonomous and seamless operation of artificial agents in human environments. Taking inspiration from biological cognition, the present work puts forward time perception as a vital capacity of artificial intelligent systems and contemplates the research path for incorporating temporal cognition in the repertoire of robotic skills.
sense of time; time processing; time perception; brain-inspired cognition; robotic systems
Autonomous manipulation in semi-structured environments where human operators can interact is an increasingly common task in robotic applications. This paper describes an intelligent multi-sensorial approach that solves this issue by providing a multi-robotic platform with a high degree of autonomy and the capability to perform complex tasks. The proposed sensorial system is composed of a hybrid visual servo control to efficiently guide the robot towards the object to be manipulated, an inertial motion capture system and an indoor localization system to avoid possible collisions between human operators and robots working in the same workspace, and a tactile sensor algorithm to correctly manipulate the object. The proposed controller employs the whole multi-sensorial system and combines the measurements of each one of the used sensors during two different phases considered in the robot task: a first phase where the robot approaches the object to be grasped, and a second phase of manipulation of the object. In both phases, the unexpected presence of humans is taken into account. This paper also presents the successful results obtained in several experimental setups which verify the validity of the proposed approach.
direct visual servo; human-robot collaboration; visual servoing; tactile control
To introduce the development of the first magnetic resonance imaging (MRI)-compatible robotic system capable of automated brachytherapy seed placement.
An MRI-compatible robotic system was conceptualized and manufactured. The entire robot was built of nonmagnetic and dielectric materials. The key technology of the system is a unique pneumatic motor that was specifically developed for this application. Various preclinical experiments were performed to test the robot for precision and imager compatibility.
The robot was fully operational within all closed-bore MRI scanners. Compatibility tests in scanners of up to 7 Tesla field intensity showed no interference of the robot with the imager. Precision tests in tissue mockups yielded a mean seed placement error of 0.72 ± 0.36 mm.
The robotic system is fully MRI compatible. The new technology allows for automated and highly accurate operation within MRI scanners and does not deteriorate the MRI quality. We believe that this robot may become a useful instrument for image-guided prostate interventions.
The paper demonstrates a following robot with omni-directional wheels, which is able to take action to avoid obstacles. The robot design is based on both fuzzy and extension theory. Fuzzy theory was applied to tune the PMW signal of the motor revolution, and correct path deviation issues encountered when the robot is moving. Extension theory was used to build a robot obstacle-avoidance model. Various mobile models were developed to handle different types of obstacles. The ultrasonic distance sensors mounted on the robot were used to estimate the distance to obstacles. If an obstacle is encountered, the correlation function is evaluated and the robot avoids the obstacle autonomously using the most appropriate mode. The effectiveness of the proposed approach was verified through several tracking experiments, which demonstrates the feasibility of a fuzzy path tracker as well as the extensible collision avoidance system.
extension theory; fuzzy theory; obstacle-avoidance; omni-directional mobile robot; ultrasonic sensors
Since the 17-century, scientists have been enquiring for the logical scientific principles of medicine and informatics, among other disciplines, encouraged by the instance of Newtonian physics. In the 20-century, the main principles of informatics were found making possible the development of present computers & Internet. However, very little research has been done seeking medical & health scientific principles, allowing among other functions, assistance in scientific hypotheses formation beside empirical data. One important effort on hypothesis formulation, has been the running of the Arrowsmith system of software and database search strategies at http://kiwi.uchicago.edu (Swanson & Smalheiser, 1997), which evokes hypothesis using the relational structure of the NCBI PubMed Internet on-line database (1966-). Nevertheless, although it uses a powerful logical mathematical method, it does not include any logical scientific principle from experimental or clinical medicine, & public health sciences. The aim of this paper is to give an outline of the design & rationale of an international collaborative research, complementary to Arrowsmith system, whose outcomes would be the logical basis of content seeking a more rational discovery support system.
Crucial fragmented information of multiple specialities and cognitive levels, synthesised by an international cross-disciplinary team or teams of experts, through a complex inductive method using Internet research facilities.
Medical & health unifying principles that would perfect Arrowsmith target search strategies or other formal discovery computer-assisted systems to formulate recombinant hypotheses, using PubMed on-line database, and even in the future, the NCBI E-Biomed Internet on-line database proposed at http://www.nih.gov/welcome/director/ebiomed/ebiomed.htm (Varmus, Lipman & Brown, 1999). The perfected system will complete then, the premises to receive the benefits of Artificial Intelligence concepts & tools, to continue its improving.
Unifying Principles; Inductive Method; Hypothesis Formulation; Internet; Discover Support System
Social intelligence in robots has a quite recent history in artificial intelligence and robotics. However, it has become increasingly apparent that social and interactive skills are necessary requirements in many application areas and contexts where robots need to interact and collaborate with other robots or humans. Research on human–robot interaction (HRI) poses many challenges regarding the nature of interactivity and ‘social behaviour’ in robot and humans. The first part of this paper addresses dimensions of HRI, discussing requirements on social skills for robots and introducing the conceptual space of HRI studies. In order to illustrate these concepts, two examples of HRI research are presented. First, research is surveyed which investigates the development of a cognitive robot companion. The aim of this work is to develop social rules for robot behaviour (a ‘robotiquette’) that is comfortable and acceptable to humans. Second, robots are discussed as possible educational or therapeutic toys for children with autism. The concept of interactive emergence in human–child interactions is highlighted. Different types of play among children are discussed in the light of their potential investigation in human–robot experiments. The paper concludes by examining different paradigms regarding ‘social relationships’ of robots and people interacting with them.
social robots; human–robot interaction; robotiquette; robot companion
In recent years the concept of "translational medicine" has been advanced in an attempt to catalyze the medical applications of basic biomedical research. However, there has been little discussion about the readiness of scientists themselves to respond to what we believe is a required new approach to scientific discovery if this new concept is to bear fruit. The present paradigm of hypothesis-driven research poorly suits the needs of biomedical research unless efforts are spent in identifying clinically relevant hypotheses. The dominant funding system favors hypotheses born from model systems and not humans, bypassing the Baconian principle of relevant observations and experimentation before hypotheses. Here, we argue that that this attitude has born two unfortunate results: lack of sufficient rigor in selecting hypotheses relevant to human disease and limitations of most clinical studies to certain outcome parameters rather than expanding knowledge of human pathophysiology; an illogical approach to translational medicine. If we wish to remain true to our responsibility and duty of performing research relevant to human disease, we must begin to think about fundamental new approaches.
NIH is the nation's medical research agency - making important medical discoveries that improve health and save lives.
NIH is the steward of medical and behavioral research for the Nation. Its mission is science in pursuit of fundamental knowledge about the nature and behavior of living systems and the application of that knowledge to extend healthy life and reduce the burdens of illness and disability .
Reinforcement learning is a computational framework
for an active agent to learn behaviors on the basis
of a scalar reward signal. The agent can be an
animal, a human, or an artificial system such as a robot or a computer program.
The reward can be food, water, money, or whatever measure of the performance
of the agent. The theory of reinforcement learning, which was developed in
an artificial intelligence community with intuitions from animal learning
theory, is now giving a coherent account on the function of the basal ganglia.
It now serves as the “common language” in which biologists,
engineers, and social scientists can exchange their problems and findings.
This article reviews the basic theoretical framework of reinforcement learning
and discusses its recent and future contributions toward the understanding
of animal behaviors and human decision making.
Maintenance tasks are crucial for all kind of industries, especially in extensive industrial plants, like solar thermal power plants. The incorporation of robots is a key issue for automating inspection activities, as it will allow a constant and regular control over the whole plant. This paper presents an autonomous robotic system to perform pipeline inspection for early detection and prevention of leakages in thermal power plants, based on the work developed within the MAINBOT (http://www.mainbot.eu) European project. Based on the information provided by a thermographic camera, the system is able to detect leakages in the collectors and pipelines. Beside the leakage detection algorithms, the system includes a particle filter-based tracking algorithm to keep the target in the field of view of the camera and to avoid the irregularities of the terrain while the robot patrols the plant. The information provided by the particle filter is further used to command a robot arm, which handles the camera and ensures that the target is always within the image. The obtained results show the suitability of the proposed approach, adding a tracking algorithm to improve the performance of the leakage detection system.
thermal image; leak detection; particle filter; tracking
Social robotics is a thriving field in building artificial agents. The possibility to construct agents that can engage in meaningful social interaction with humans presents new challenges for engineers. In general, social robotics has been inspired primarily by psychologists with the aim of building human-like robots. Only a small subcategory of “companion robots” (also referred to as robotic pets) was built to mimic animals. In this opinion essay we argue that all social robots should be seen as companions and more conceptual emphasis should be put on the inter-specific interaction between humans and social robots. This view is underlined by the means of an ethological analysis and critical evaluation of present day companion robots. We suggest that human–animal interaction provides a rich source of knowledge for designing social robots that are able to interact with humans under a wide range of conditions.
social robotics; ethology; human–animal interaction; dogs; inter-specific interaction
The indexing of scientific literature and content is a relevant and contemporary requirement within life science information systems. Navigating information available in legacy formats continues to be a challenge both in enterprise and academic domains. The emergence of semantic web technologies and their fusion with artificial intelligence techniques has provided a new toolkit with which to address these data integration challenges. In the emerging field of lipidomics such navigation challenges are barriers to the translation of scientific results into actionable knowledge, critical to the treatment of diseases such as Alzheimer's syndrome, Mycobacterium infections and cancer.
We present a literature-driven workflow involving document delivery and natural language processing steps generating tagged sentences containing lipid, protein and disease names, which are instantiated to custom designed lipid ontology. We describe the design challenges in capturing lipid nomenclature, the mandate of the ontology and its role as query model in the navigation of the lipid bibliosphere. We illustrate the extent of the description logic-based A-box query capability provided by the instantiated ontology using a graphical query composer to query sentences describing lipid-protein and lipid-disease correlations.
As scientists accept the need to readjust the manner in which we search for information and derive knowledge we illustrate a system that can constrain the literature explosion and knowledge navigation problems. Specifically we have focussed on solving this challenge for lipidomics researchers who have to deal with the lack of standardized vocabulary, differing classification schemes, and a wide array of synonyms before being able to derive scientific insights. The use of the OWL-DL variant of the Web Ontology Language (OWL) and description logic reasoning is pivotal in this regard, providing the lipid scientist with advanced query access to the results of text mining algorithms instantiated into the ontology. The visual query paradigm assists in the adoption of this technology.
This paper presents a state of the art of the estimation algorithms dealing with Out-of-Sequence (OOS) measurements for non-linearly modeled systems. The state of the art includes a critical analysis of the algorithm properties that takes into account the applicability of these techniques to autonomous mobile robot navigation based on the fusion of the measurements provided, delayed and OOS, by multiple sensors. Besides, it shows a representative example of the use of one of the most computationally efficient approaches in the localization module of the control software of a real robot (which has non-linear dynamics, and linear and non-linear sensors) and compares its performance against other approaches. The simulated results obtained with the selected OOS algorithm shows the computational requirements that each sensor of the robot imposes to it. The real experiments show how the inclusion of the selected OOS algorithm in the control software lets the robot successfully navigate in spite of receiving many OOS measurements. Finally, the comparison highlights that not only is the selected OOS algorithm among the best performing ones of the comparison, but it also has the lowest computational and memory cost.
autonomous mobile robots; location estimation; out-of-sequence; extended Kalman filter
Service robotics has increasingly become the focus of reflective research on new technologies over the last decade. The current state of technology is characterized by prototypical robot systems developed for specific application scenarios outside factories. This has enabled context-based Science and Technology Studies and technology assessments of service robotic systems. This contribution describes the status quo of this reflective research as the starting point for interdisciplinary technology assessment (TA), taking account of TA studies and, in particular, of publications from the ethical and empirical social science perspective. Finally, based on this status quo, evaluation criteria for service robots are developed, which are relevant for further reflective research.
Advanced technology has made it possible to build machines and systems like robots, which are capable of making intelligent decisions.
Robots capable of self-replication and perform human functions are also available. The current challenge is to design evolutionary systems
with high complexity comparable to that of biological networks. This is proposed to be achieved by ALife (Artificial Life). Here, we describe
the promises provided by ALife for life sciences.
cellular automaton; synthetic biology; artificial intelligence; artificial life
The term “robot” was coined by the Czech playright Karel Capek in 1921 in his play Rossom's Universal Robots. The word “robot” is from the check word robota which means forced labor. The era of robots in surgery commenced in 1994 when the first AESOP (voice controlled camera holder) prototype robot was used clinically in 1993 and then marketed as the first surgical robot ever in 1994 by the US FDA. Since then many robot prototypes like the Endoassist (Armstrong Healthcare Ltd., High Wycombe, Buck, UK), FIPS endoarm (Karlsruhe Research Center, Karlsruhe, Germany) have been developed to add to the functions of the robot and try and increase its utility. Integrated Surgical Systems (now Intuitive Surgery, Inc.) redesigned the SRI Green Telepresence Surgery system and created the daVinci Surgical System® classified as a master-slave surgical system. It uses true 3-D visualization and EndoWrist®. It was approved by FDA in July 2000 for general laparoscopic surgery, in November 2002 for mitral valve repair surgery. The da Vinci robot is currently being used in various fields such as urology, general surgery, gynecology, cardio-thoracic, pediatric and ENT surgery. It provides several advantages to conventional laparoscopy such as 3D vision, motion scaling, intuitive movements, visual immersion and tremor filtration. The advent of robotics has increased the use of minimally invasive surgery among laparoscopically naïve surgeons and expanded the repertoire of experienced surgeons to include more advanced and complex reconstructions.
Robotic surgery; da Vinci surgery
The 50-year global CO2 record led the way in establishing a scientific fact: modern civilization is changing important properties of the global atmosphere, oceans and biosphere. The evidence on which this scientific fact is based will be refined further, but the next challenge for scientists is broader. In addition to its traditional role in providing discovery, diagnosis, and prediction of the changes that are taking place on our planet, science has now also a role in helping society mitigate emissions by objectively quantifying them, and in helping adaptation by providing environmental forecasts on regional scales. Science is also expected to provide new options for society to tackle the transition to a new energy system, and to provide thorough environmental evaluation of all such options. This is what the meeting recognized as planetary responsibilities for scientists in the next 50 years.
The objective of this article was to review the published scientific literature pertaining to robotic surgery and its applications in gynecologic malignancies and to summarize the impact of robotic surgery on the field of gynecologic oncology. Summarizing data from different gynecologic disease-sites, robotic-assisted surgery is safe, feasible, and demonstrates equivalent histopathologic and oncologic outcomes. In general, benefits to robotic surgery include decreased blood loss, fewer perioperative complications and decreased length of hospital stay. Disadvantages include accessibility to robot surgical systems, decreased haptic sensation and fixed cost as well as cost of disposable equipment. As robotic surgery becomes readily available it will be imperative to develop standardized training modalities. Further research is needed to validate the role of robotic surgery in the treatment of gynecologic malignancies.
Gynecologic malignancies; Robotic surgery
Translation of pharmacogenomics to public health action is at the epicenter of the life sciences agenda. Post-genomics knowledge is simultaneously co-produced at multiple scales and locales by scientists, crowd-sourcing and biological citizens. The latter are entrepreneurial citizens who are autonomous, self-governing and increasingly conceptualizing themselves in biological terms, ostensibly taking responsibility for their own health, and engaging in patient advocacy and health activism. By studying these heterogeneous 'scientific cultures', we can locate innovative parameters of collective action to move pharmacogenomics to practice (personalized therapeutics). To this end, we reconceptualize knowledge-based innovation as a complex ecosystem comprising 'actors' and 'narrators'. For robust knowledge translation, we require a nested post-genomics technology governance system composed of first-order narrators (for example, social scientists, philosophers, bioethicists) situated at arm's length from innovation actors (for example, pharmacogenomics scientists). Yet, second-order narrators (for example, an independent and possibly crowd-funded think-tank of citizen scholars, marginalized groups and knowledge end-users) are crucial to prevent first-order narrators from gaining excessive power that can be misused in the course of steering innovations. To operate such 'self-calibrating' and nested innovation ecosystems, we introduce the concept of 'wiki-governance' to enable mutual and iterative learning among innovation actors and first- and second-order narrators.
'[A] scientific expert is someone who knows more and more about less and less, until finally knowing (almost) everything about (almost) nothing.' 
'Ubuntu: I am because you are.' 
The role of basic science exposure during urology training is a timely topic that is relevant to urologic health and to the training of new physician scientists. Today, researchers are needed for the advancement of this specialty, and involvement in basic research will foster understanding of basic scientific concepts and the development of critical thinking skills, which will, in turn, improve clinical performance. If research education is not included in urology training, future urologists may not be as likely to contribute to scientific discoveries.
Currently, only a minority of urologists in training are currently exposed to significant research experience. In addition, the number of physician-scientists in urology has been decreasing over the last two decades, as fewer physicians are willing to undertake a career in academics and perform basic research. However, to ensure that the field of urology is driving forward and bringing novel techniques to patients, it is clear that more research-trained urologists are needed. In this article we will analyse the current status of basic research in urology training and discuss the importance of and obstacles to successful addition of research into the medical training curricula. Further, we will highlight different opportunities for trainees to obtain significant research exposure in urology.
clinician-scientist; discovery; translation; funding