Multiple Intelligent Systems Research Articles

Intelligent Mining Road Object Detection Based on Multiscale Feature Fusion in Multi-UAV Networks

In complex mining environments, driverless mining trucks are required to cooperate with multiple intelligent systems. They must perform obstacle avoidance based on factors such as the site road width, obstacle type, vehicle body movement state, and ground concavity-convexity. Targeting the open-pit mining area, this paper proposes an intelligent mining road object detection (IMOD) model developed using a 5G-multi-UAV and a deep learning approach. The IMOD model employs data sensors to monitor surface data in real time within a multisystem collaborative 5G network. The model transmits data to various intelligent systems and edge devices in real time, and the unmanned mining card constructs the driving area on the fly. The IMOD model utilizes a convolutional neural network to identify obstacles in front of driverless mining trucks in real time, optimizing multisystem collaborative control and driverless mining truck scheduling based on obstacle data. Multiple systems cooperate to maneuver around obstacles, including avoiding static obstacles, such as standing and lying dummies, empty oil drums, and vehicles; continuously avoiding multiple obstacles; and avoiding dynamic obstacles such as walking people and moving vehicles. For this study, we independently collected and constructed an obstacle image dataset specific to the mining area, and experimental tests and analyses reveal that the IMOD model maintains a smooth route and stable vehicle movement attitude, ensuring the safety of driverless mining trucks as well as of personnel and equipment in the mining area. The ablation and robustness experiments demonstrate that the IMOD model outperforms the unmodified YOLOv5 model, with an average improvement of approximately 9.4% across multiple performance measures. Additionally, compared with other algorithms, this model shows significant performance improvements.

Optical Nanofiber Skins for Multifunctional Humanoid Tactility

Humanoid tactility has been boosting robotic intelligence in object recognition, dexterous manipulation, and human–robot interaction. For many artificial tactile sensors, especially those based on optical principles, inflexibility, bulkiness, and monomodality limit their potential to function as humanoid skins. Herein, by embedding lab‐made optical nanofibers (ONFs) into elastomeric films, soft, flexible, thin (around 500 μm, similar to human skin), and multimodal (force and thermosensitive) robotic skins are achieved. These superior characteristics arise from the low flexural rigidity and large evanescent field of ONFs, due to their subwavelength diameters (down to 450 nm). By tuning light wavelength, ONF diameter, and skin thickness, variable sensitivities and sensing ranges for both force and temperature are reported. Depending on different sensing requirements, special modules can be further assembled on the ONF skins for various surface properties, including hardness, texture, and thermal conductivity. The ONF skins can enable a commercial robot to emulate human behaviors, including adaptive grasping of flimsy objects, contactless temperature measurement, and even the perception of leaf veins. It is anticipated that these ONF skins could offer a unique solution in multiple intelligent systems such as robotics, prosthetics, human–machine interface and wearable devices.

Fused and Modified Evolutionary Optimization of Multiple Intelligent Systems Using ANN, SVM approaches

The Fused Modified Grasshopper Optimization Algorithm has been proposed, which selects the most specific feature sets from images of the disease of plant leaves. The Proposed algorithm ensures the detection of diseases during the early stages of the diagnosis of leaf disease by farmers and, finally, the crop needed to be controlled by farmers to ensure the survival and protection of plants. In this study, a novel approach has been suggested based on the standard optimization algorithm for grasshopper and the selection of features. Leaf conditions in plants are a major factor in reducing crop yield and quality. Any delay or errors in the diagnosis of the disease can lead to delays in the management of plant disease spreading and damage and related material losses. Comparative new heuristic optimization of swarm intelligence, Grasshopper Optimization Algorithm was inspired by grasshopper movements for their feeding strategy. It simulates the attitude and social interaction of grasshopper swarm in terms of gravity and wind advection. In the decision on features extracted by an accelerated feature selection algorithm, popular approaches such as ANN and SVM classifiers had been used. For the evaluation of the proposed model, different data sets of plant leaves were used. The proposed model was successful in the diagnosis of the diseases of leaves the plant with an accuracy of 99.41 percent (average). The proposed biologically inspired model was sufficiently satisfied, and the best or most desirable characteristics were established. Finally, the results of the research for these data sets were estimated by the proposed Fused Modified Grasshopper Optimization Algorithm (FMGOA). The results of that experiment were demonstrated to allow classification models to reduce input features and thus to increase the precision with the presented Modified Grasshopper Optimization Algorithm. Measurement and analysis were performed to prove the model validity through model parameters such as precision, recall, f-measure, and precision.

Ontologies for Industry 4.0

Abstract The current fourth industrial revolution, or ‘Industry 4.0’ (I4.0), is driven by digital data, connectivity, and cyber systems, and it has the potential to create impressive/new business opportunities. With the arrival of I4.0, the scenario of various intelligent systems interacting reliably and securely with each other becomes a reality which technical systems need to address. One major aspect of I4.0 is to adopt a coherent approach for the semantic communication in between multiple intelligent systems, which include human and artificial (software or hardware) agents. For this purpose, ontologies can provide the solution by formalizing the smart manufacturing knowledge in an interoperable way. Hence, this paper presents the few existing ontologies for I4.0, along with the current state of the standardization effort in the factory 4.0 domain and examples of real-world scenarios for I4.0.

Modeling of Interaction of Multiple Intelligent Systems in a Real-World Environment

AbstractCore of this contribution is the modeling of interaction between human operators and technical systems (both referred to as Intelligent Systems (IS)) working cooperatively in real-world environments such as automobile traffic or production processes. Consequently, knowledge about the process and other involved IS has to be considered. Current approaches consider only a single IS interacting with a dynamically changing environment, or assume that the IS acts independently and not as part of a group. In this contribution, two possible approaches to model the interaction between multiple IS and the environment are discussed. On the one hand, the whole task with all interacting IS is modeled from an independent and neutral perspective (Integral Approach); on the other hand, each IS could be modeled independently from each other including an individual model of the perceived others (Empathy Approach). As underlying meta modeling technique for both approaches, the Situation-Operator-Modeling-technique (SOM) is used. Both approaches are illustrated using a simplified example of a traffic situation. The advantages and disadvantages of both approaches are discussed.

AI for Herding Sheep

Shepherding with a dog presents an interesting challenge for artificial intelligence, with multiple intelligent systems assessing and interacting with each other in order to achieve a variety of goals. We present a solution to this problem, which consists of a dog AI making use of influence mapping, state machines and A* pathfinding to respond intelligently to real-life shepherding commands issued by a high-level shepherd AI steering the flock of sheep through waypoints on a variety of maps by using pathfinding and influence maps. The role of the AI shepherd can also be taken by a human player (using either a point and click or voice recognition interface) for matches against the artificial shepherd which proved to be a worthy opponent for human testers. The system was evaluated through user testing and provided a high degree of realism and engaging gameplay relying heavily on the workings of the presented AI components.

MACE: High-level distributed objects in a flexible testbed for distributed AI research

Distributed Artificial Intelligence (DAI) is a subfield of AI concerned with the problems of describing and constructing multiple intelligent systems which interact. MACE (for Multi-Agent Computing Environment) is a language, programming environment, and testbed for DAI systems. Parallelism in AI problem-solving applications can be exploited at many different levels: in hardware, in the implementation language (e.g., a production system language), in a problem-solving paradigm, or directly in the application. MACE is an instrumented testbed for building a wide range of experimental Distributed Artificial Intelligence systems. The goal of MACE is to support experimentation with different styles of distributed AI systems, at different levels of complexity. The dominant metaphor of MACE is a collection of intelligent, semi-autonomous agents interacting in organized ways. MACE is a distributed, object-oriented system. MACE computational units (called agents) run in parallel, and communicate via messages. They provide optional facilities for knowledge representation (world knowledge, models of other agents, their goals and plans, their roles and capabilities, etc.) and reasoning capabilities. The MACE environment maps agents onto processors, handles inter-agent communication, and provides several features: A collection of agents. Agents are the basic computational units of the MACE system. Rather than being simple generic object structures, MACE agents are inherently “social” in nature: they know about some other agents in their environment, and expect to draw upon and coordinate with the expertise of others they know about. MACE agents represent this information in the form of models of other agents in the world in a special database called acquaintances , which is a property of each MACE agent. Current agent models are predicate-calculus based. MACE agents may be composed into sub-units or coalitions; they respond as organized groups or composites. The MACE Agent Description Language includes facilities for describing organized clusters of agents. A community of system agents. A number of pre-defined system agents provide MACE command interpretation, a standard user interface, the agent-builder cluster, error handling, tracing, execution monitoring, etc. Specific MACE agents serve as interactive tools for building and executing MACE programs. A collection of facilities which all agents may use, including a pattern matcher, a simulator, several standard agent engines, handlers for standard errors, and standard messages understood by all agents. MACE incorporates extensive pattern-matching facilities for interpreting messages, for pattern-directed invocation of asynchronous events (e.g., demons and other event-monitors), and for associative database access within agents. Instrumentation. The MACE testbed is instrumented to allow for measurements of the characteristics of problem-solvers during experimental runs. Message traffic, queue and database sizes, work done by an agent (in terms of elapsed real time or number of invocations), and load on a processor node are common measurements. A description database. Agent descriptions are maintained in a description database by a system-agent cluster which constructs new descriptions, verifies descriptions, and constructs executable agents from descriptions. MACE enforces a separation of description and execution to handle some problems of distribution of objects. A collection of kernels. MACE kernels collectively handle communication and message routing, perform I/O to terminals, files, or other devices, map agents onto processors, and schedule agents for execution. Reflection capabilities. A MACE agent's acquaintances database can include, of course, models of the behaviors, plans, roles, or implementation of the agent itself, providing high-level and behavioral reflection capabilities. An evolutionary philosophy. MACE agents are intended to be a high-level approach to distributed object-oriented programming. Since we are using MACE as a testbed to explore coordination strategies for intelligent multi-agent systems, the nature of generic MACE agents changes and specializes over time. All MACE agents now have acquaintance databases, react to certain specialized messages, and include particular attributes (e.g. the attribute “ME” which includes a partial self-description). Over time, as more coordination and modeling features are found useful, they will be standardized as features of MACE agents. In this way, MACE agents are explicitly evolving toward higher-level objects. MACE has been used to build numerous experimental DAI systems including models of lower-level parallelism (sets of distributed production rule systems without global database or inference engine, where each rule is an agent) and higher-level distributed problem-solving architectures (e.g., domain-independent distributed blackboard and contract-net schemes). MACE is implemented on a 16-node INTEL iPSC/1 large-memory hypercube and in a LISP machine environment, and will soon be available on the iPSC/2 and a Sun network.