1,721,361 research outputs found
Computational Approaches to Conscious Artificial Intelligence
No full textArtificial Intelligence (AI) has seen rapid advancements in recent years, particularly in the areas of deep learning and the ability to generalize from concrete objects to abstract concepts. Meanwhile, in the study of machine consciousness, a universally agreed definition among scientists and philosophers is still lacking.
This book raises a number of issues surrounding the nature and implications of conscious artificial intelligence:
How might concepts developed through research in machine consciousness enhance the functionality of AI systems, making them fully autonomous?
If self-explanatory information is related to qualia, does this imply that machines using self-explanatory information are conscious?
How is the interpretation of intelligence related to a system's ability to generate accurate predictions?
Is machine consciousness ultimately possible? And how might it be examined from an ethical standpoint?
This edited volume consists of 10 chapters that highlight the prospects of machine consciousness and study the subject from several perspectives. The issues are wide-ranging and include topics such as the metaverse, a computational approach to pain and suffering, universal cognitive intelligence, intentional action, the categorization of conscious machines, and more. The volume is designed as a reference guide for researchers, practitioners, and students interested in the intersection of AI and consciousness
Computing fast search heuristics for physics-based mobile robot motion planning
Full text linkMobile robots are increasingly being employed to assist responders in search and rescue missions. Robots have to navigate in dangerous areas such as collapsed buildings and hazardous sites, which can be inaccessible to humans. Tele-operating the robots can be stressing for the human operators, which are also overloaded with mission tasks and coordination overhead, so it is important to provide the robot with some degree of autonomy, to lighten up the task for the human operator and also to ensure robot safety.
Moving robots around requires reasoning, including interpretation of the environment, spatial reasoning, planning of actions (motion), and execution. This is particularly challenging when the environment is unstructured, and the terrain is \textit{harsh}, i.e. not flat and cluttered with obstacles.
Approaches reducing the problem to a 2D path planning problem fall short, and many of those who reason about the problem in 3D don't do it in a complete and exhaustive manner.
The approach proposed in this thesis is to use rigid body simulation to obtain a more truthful model of the reality, i.e. of the interaction between the robot and the environment. Such a simulation obeys the laws of physics, takes into account the geometry of the environment, the geometry of the robot, and any dynamic constraints that may be in place.
The physics-based motion planning approach by itself is also highly intractable due to the computational load required to perform state propagation combined with the exponential blowup of planning; additionally, there are more technical limitations that disallow us to use things such as state sampling or state steering, which are known to be effective in solving the problem in simpler domains.
The proposed solution to this problem is to compute heuristics that can bias the search towards the goal, so as to quickly converge towards the solution.
With such a model, the search space is a rich space, which can only contain states which are physically reachable by the robot, and also tells us enough information about the safety of the robot itself.
The overall result is that by using this framework the robot engineer has a simpler job of encoding the \textit{domain knowledge} which now consists only of providing the robot geometric model plus any constraints
A cognitive architecture for inner speech
Full text linkA cognitive architecture for inner speech is presented. It is based on the Standard Model of Mind, integrated with modules for self- talking. Briefly, the working memory of the proposed architecture includes the phonological loop as a component which manages the exchanging information between the phonological store and the articulatory control system. The inner dialogue is modeled as a loop where the phonological store hears the inner voice produced by the hidden articulator process. A central executive module drives the whole system, and contributes to the generation of conscious thoughts by retrieving information from long-term memory. The surface form of thoughts thus emerges by the phonological loop. Once a conscious thought is elicited by inner speech, the perception of new context takes place and then repeating the cognitive loop. A preliminary formalization of some of the described processes by event cal- culus, and early results of their implementation on the humanoid robot Pepper by SoftBank Robotics are discussed
A calculus for robot inner speech and self-awareness
Full text linkThe inner speech is the common mental experience the humans have when they dialogue with themselves. It is widely acknowledged that inner speech is related to awareness and self-awareness. The inner speech reproduces and expands in the mind social and physical sources of awareness. In this preliminary work, a calculus based on a first-order modal logic to automate inner speech is presented. It attempts to make the existing inner speech theories suitable for robot. By making robot able to talk to itself, it is possible to analyze the role of inner speech in robot awareness and self-awareness, opening new interesting research scenarios not yet investigated
Quantum RoboSound: Auditory Feedback of a Quantum-Driven Robotic Swarm
No full textData sonification enhance and enrich information understanding with an additional sensory dimension. Sonification also opens the way to more creative applications, joining arts and sciences. In this study, we present sequences of chords obtained as auditory feedback from the trajectories of a robotic swarm. The swarm behavior is an emerging effect from simple local interactions and autonomous decisions of each robot. The swarm effect can be identified through sonification outcomes in terms of voice leading patterns. Thus, chord patterns represent behavior patterns. The convergence to the target is represented by the convergence to a specific pitch. The swarm decision process is based upon quantum computing. We first present logic gates and their implementations as quantum circuits, describing examples with 2- and 3-dimensional motion of a 3-robot toy swarm. The considered scenarios are ant foraging (2D) and underwater search and rescue (3D). Then, we provide and discuss some examples of the harmonic sequences that can be obtained as feedback from robotic motion
A cognitive architecture for artificial vision
No full textAbstractA new cognitive architecture for artificial vision is proposed. The architecture, aimed at an autonomous intelligent system, is cognitive in the sense that several cognitive hypotheses have been postulated as guidelines for its design. The first one is the existence of a conceptual representation level between the subsymbolic level, that processes sensory data, and the linguistic level, that describes scenes by means of a high level language. The conceptual level plays the role of the interpretation domain for the symbols at the linguistic levels. A second cognitive hypothesis concerns the active role of a focus of attention mechanism in the link between the conceptual and the linguistic level: the exploration process of the perceived scene is driven by linguistic and associative expectations. This link is modeled as a time delay attractor neural network. Results are reported obtained by an experimental implementation of the architecture
Does Creativity Help Us Survive? A Possible Approach with Quantum-Driven Robots
Full text linkHow can we relate quantum computing, robotics, and music? This is a position paper where we try to connect these fields. We discuss one main question regarding computational creativity and we imagine an experimental setup to test our hypothesis. Artificial intelligence and autonomous machines can accomplish simple creative tasks such as reorganizing given material. Because creativity is a human (and not only) resource to survive, we wonder if also artificial agents, such as robots, might develop creativity at a higher level to ensure self-survival. Then, we design an experimental setup with three robots, playing and dancing. If music and movement flow is regular (with the beat difference below a certain threshold), the voltage given to robots is constant. Otherwise, if there are inconsistencies, it drops, activating robots’ alert signal sensors, and triggering new musical activity. We use the paradigm of quantum computing to formalize our claims. This test might be performed in situ in a robotic lab
Conceptual Spaces for Cognitive Architectures: A lingua franca for different levels of representation
No full textDuring the last decades, many Cognitive Architectures (CAs) have been realized adopting different assumptions about the organization and the representation of their knowledge level. Some of them (e.g. SOAR (Laird, 2012)) adopt a classical symbolic approach, some (e.g. LEABRA O'Reilly and Munakata (2000)) are based on a purely connectionist model, while others (e.g. CLARION (Sun, 2006)) adopt a hybrid approach combining connectionist and symbolic representational levels. Additionally, some attempts (e.g. biSOAR) trying to extend the representational capacities of CAs by integrating diagrammatical representations and reasoning are also available (Kurup & Chandrasekaran, 2007). In this paper we propose a reflection on the role that Conceptual Spaces, a framework developed by Gärdenfors (2000) more than fifteen years ago, can play in the current development of the Knowledge Level in Cognitive Systems and Architectures. In particular, we claim that Conceptual Spaces offer a lingua franca that allows to unify and generalize many aspects of the symbolic, sub-symbolic and diagrammatic approaches (by overcoming some of their typical problems) and to integrate them on a common ground. In doing so we extend and detail some of the arguments explored by Gärdenfors (1997) for defending the need of a conceptual, intermediate, representation level between the symbolic and the sub-symbolic one. In particular we focus on the advantages offered by Conceptual Spaces (with respect to symbolic and sub-symbolic approaches) in dealing with the problem of compositionality of representations based on typicality traits. Additionally, we argue that Conceptual Spaces could offer a unifying framework for interpreting many kinds of diagrammatic and analogical representations. As a consequence, their adoption could also favor the integration of diagrammatical representation and reasoning in CAs
Agents and robots for collaborating and supporting physicians in healthcare scenarios
No full textMonitoring patients through robotics telehealth systems is an interesting scenario where patients’ conditions, and their environment, are dynamic and unknown variables. We propose to improve telehealth systems’ features to include the ability to serve patients with their needs, operating as human caregivers. The objective is to support the independent living of patients at home without losing the opportunity to monitor their health status. Application scenarios are several, and they spread from simple clinical assisting scenarios to an emergency one. For instance, in the case of a nursing home, the system would support in continuously monitoring the elderly patients. In contrast, in the case of an epidemic diffusion, such as COVID-19 pandemic, the system may help in all the early triage phases, significantly reducing the risk of contagion. However, the system has to let medical assistants perform actions remotely such as changing therapies or interacting with patients that need support. The paper proposes and describes a multi-agent architecture for intelligent medical care. We propose to use the beliefs-desires-intentions agent architecture, part of it is devised to be deployed in a robot. The result is an intelligent system that may allow robots the ability to select the most useful plan for unhandled situations and to communicate the choice to the physician for his validation and permission
Conceptual representations of actions for autonomous robots
No full textAn autonomous robot involved in long and complex missions should be able to generate, update and process its own plans of action. In this perspective, it is not plausible that the meaning of the representations used by the robot is given from outside the system itself. Rather, the meaning of internal symbols must be firmly anchored to the world through the perceptual abilities and the overall activities of the robot. According to these premises, in this paper we present an approach to action representation that is based on a "conceptual" level of representation, acting as an intermediate level between symbols and data coming from sensors. Symbolic representations are interpreted by mapping them on the conceptual level through a mapping mechanism based on artificial neural networks. Examples of the proposed framework are reported, based on experiments performed on a RWI-B12 autonomous robot. © 2001 Elsevier Science B.V
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