Orchestrated multi agents sustain accuracy under clinical-scale workloads compared to a single agent

Control of time-delay systems represents one important class of control problems. A lot of work has been done in recent years. Much of the research work has been focused on the stability analysis and stabilization of time-delay systems based on the Lyapunov functional and linear matrix inequality (LMI) approaches. While the LMI approach does provide an e±cient tool, the results are mostly only su±cient and only numerical solutions are available. Our research aims to present analytical solutions by some algebraic approaches, which can provide an insightful understanding of control problem of time-delay systems. On the other hand, there has also been significant recent interest in networked multi-agent system (MAS) where unavoidably there exist delays in information acquisition as well as information exchange between agents in addition to possible input/state delays in each agent. This motivates the study of MASs with input and communication delays. The thesis is divided into two parts: Part I is focused on optimal control of single agent systems with multiple input delays; Part II is on distributed consensus control of MAS with communication and input delays. More specifically, in Part I, we shall study the linear quadratic regulation (LQR) and tracking for a single agent with linear dynamics. We first focus on LQR for linear discrete-time systems with multiple delays in a single input channel. By introducing a backward stochastic system, we establish a relationship between the LQR problem for the original system and optimal estimation for a backward stochastic system which can be solved by using projection approach. The explicit optimal controllers are developed for both finite time and infinite time horizon cases. A parallel result for linear continuous-time systems is also established, where the optimal feedback gain is given in terms of the solution of Riccati-type partial differential equations (PDEs).

A New Approach In Learning For Intelligent Multi Agent Systems

This paper studies the stability and performance of a multi‐agent system composed of identical linear time‐invariant dynamic agents, when only a single agent in the system is controlled by an exogenous single‐input–single‐output controller. It is shown that a wide class of multi‐agent systems can be collectively stabilised by controlling any single agent. The tracking performance of such a closed‐loop system, however, has a fundamental limitation. It is also shown that generically not only the output of controlled agent but either the sum or average of all agent outputs can be used for an exogenous controller. An example is presented to illustrate theoretical results developed in this study.

The article focuses on a type of client/server multiagent system. Such system consists of several server environments, each populated by a single server agent and multiple client agents. We propose that a server agent should monitor clients activity in order to perform better. We employ the term agent tracking, introduced by Tambe and Rosenbloom. We introduce the idea of distributed tracking. Our multiagent system includes client tracking agents, which are responsible for agent tracking of clients and are devoted to a server. Each tracking agent executes a single model, which can apply to a single client agent or a group of client agents. All tracking agents work cooperatively and make collective decisions about formation of client groups. Tracking agents derive facts about client behavior and supply the server with conclusions. Generalized nets (GNs - an extension of Petri nets) are used as a tool for modeling multiagent system. A GN model of a single server environment with tracking agents is described.

The concept of agent has been successfully used in a wide range of applications such as Robotics, e-commerce, agent-assisted user training, military transport or health-care. The origin of this concept can be located in 1977, when Carl Hewitt proposed the idea of an interactive object called actor. This actor was defined as a computational agent, which has a mail address and a behaviour (Hewitt, 1977). Actors receive messages from other actors and carry out their tasks in a concurrent way. It is difficult that a single agent could be sufficient to carry out a relatively complex task. The usual approach consists of a society of agents - called Multiagent Systems (MAS) -, which communicate and collaborate among them and they are coordinated when pursuing a goal. The purpose of this chapter is to analyze the aspects related to the application of MAS to System Engineering and Robotics, focusing on those approaches that combine MAS with other Artificial Intelligence (AI) techniques.

In the process of road surface construction, dispatchers' scheduling was experiential and blindfold in some degree and static scheduling restricted the continuity of the construction. Serious problems such as labor holdup, material awaiting and scheduling delay could occur when the old scheduling technique was used. This paper presents intelligent algorithm based on multi-agent system(MAS) that has the abilities of intelligentized modeling and dynamic scheduling. MAS model deals with single agent's communication and corresponding in engineering machinery of clusters firstly, next we apply intelligent algorithm to solve dynamic scheduling in the plant. Intelligent algorithm can optimize the match of agents and make the system dynamic balance. The effectiveness of the proposed method is demonstrated with MATLAB simulations. At last, simulation software in road machinery based on intelligent and hybrid MAS was realized.

This thesis presents some different techniques designed to drive a swarm of robots in an a-priori unknown environment in order to move the group from a starting area to a final one avoiding obstacles. The presented techniques are based on two different theories used alone or in combination: Swarm Intelligence (SI) and Graph Theory. Both theories are based on the study of interactions between different entities (also called agents or units) in Multi- Agent Systems (MAS). The first one belongs to the Artificial Intelligence context and the second one to the Distributed Systems context. These theories, each one from its own point of view, exploit the emergent behaviour that comes from the interactive work of the entities, in order to achieve a common goal. The features of flexibility and adaptability of the swarm have been exploited with the aim to overcome and to minimize difficulties and problems that can affect one or more units of the group, having minimal impact to the whole group and to the common main target. Another aim of this work is to show the importance of the information shared between the units of the group, such as the communication topology, because it helps to maintain the environmental information, detected by each single agent, updated among the swarm. Swarm Intelligence has been applied to the presented technique, through the Particle Swarm Optimization algorithm (PSO), taking advantage of its features as a navigation system. The Graph Theory has been applied by exploiting Consensus and the application of the agreement protocol with the aim to maintain the units in a desired and controlled formation. This approach has been followed in order to conserve the power of PSO and to control part of its random behaviour with a distributed control algorithm like Consensus.

Semantic Similarity is one of the highest levels of NLP. STS has significant advantages in NLP applications like information retrieval, information extraction, text summarization, data mining, machine translation, and other tasks. This research aims to present a deep learning approach for capturing semantic textual similarity (STS) in the Guragigna. The methodology involves collecting a Guragigna language corpus and preprocessing the text data and text representation is done using the Universal Sentence Encoder (USE), along with word embedding techniques including Word2Vec and GloVe and mean Square Error (MSE) is used to measure the performance. In the experimentation phase, models like LSTM, Bidirectional RNN, GRU, and Stacked RNN are trained and evaluated using different embedding techniques. The results demonstrate the efficacy of the developed models in capturing semantic textual similarity in the Guragigna language. Across different embedding techniques, including Word2Vec, GloVe, and USE, the Bidirectional RNN model with USE embedding achieves the lowest MSE of 0.0950 and the highest accuracy of 0.9244. GloVe and Word2Vec embedding also show competitive performance with slightly higher MSE and lower accuracy. The Universal Sentence Encoder consistently emerges as the top-performing embedding across all RNN architectures. The research results demonstrate the effectiveness of LSTM, GRU, Bi RNN, and Stacked RNN models in measuring semantic textual similarity in the Guragigna language.

In current multi-agent systems, the user is typically interacting with a single agent at a time through relatively inflexible and modestly intelligent interfaces. As a consequence, these systems force the users to submit simplistic requests only and suffer from problems such as the low-level nature of the system services offered to users, the weak reusability of agents, and the weak extensibility of the systems. In this paper, a framework for multi-agent systems called the open agent architecture (OAA) which reduces such problems, is discussed. The OAA is designed to handle complex requests that involve multiple agents. In some cases of complex requests from users, the components of the requests do not directly correspond to the capabilities of various application agents, and therefore, the system is required to translate the user's model of the task into the system's model before apportioning subtasks to the agents. To maximize users' efficiency in generating this type of complex requests, the OAA offers an intelligent multi-modal user interface agent which supports a natural language interface with a mix of spoken language, handwriting, and gesture. The effectiveness of the OAA environment including the intelligent distributed multi-modal interface has been observed in our development of several practical multi-agent systems.

An intelligent multiagent system includes a number of software agents interacting to solve a problem. Various agents are responsible for planning different tasks. These tasks must be coordinated to solve complex problems. A number of reasons exist for which coordination among the agents is necessary, and numerous issues have to be tackled to achieve efficient coordination. The focus of this paper is resource coordination. When an agent lacks a resource to achieve a goal, it may choose one of two options. It can acquire another resource, or it can change the goal so that it can achieve it with the resource at hand. We present a cost-benefit analysis that can be performed to determine if the requesting agent receives the requested resource. We present empirical results that show the relative goal satisfaction achieved in the PRODIGY planner with and without resource coordination. We have implemented a multiagent system called COMAS that implements resource coordination.

In the area of biotechnology, a micromanipulation is widely used for such purposes as operating on genes and transferring biological materials into cells. For the some experiments, such as biochemical experiment, a large number of cells have to be manipulated in a short time. We have developed an automatic micromanipulation system under the stereoscopic microscope. Micromanipulation system carries out various processes, such as detection of the target, the detection of the needle head, and motor control. By sharing these processes with several computers, the micromanipulation can be performed at high speed. As a result, computer cooperation becomes very important. In this paper, we propose a multi agent micromanipulation system. At first, we developed a multi agent system, which performs image processing, motor control, and management of the micromanipulation processes. Secondarily, we proposed to operate computers cooperative. We use a computer as a single agent. And several computers are connected to a local area network. The multi agent micromanipulation system performed the micromanipulation at a realistic rate through cooperation of multi agents.

Developing a multi-agent system means describing an agent architecture and each single agent already at a conceptual level. This is the corner stone behind the CoMoMAS approach. Conceptual models are implementation-independent and reusable in the form of generic templates for the description of other systems. The present approach extends CommonKADS, a methodology for the development of KBS (see Section 4 for a detailed description) towards the modelling of multi-agent systems. CommonKADS is the result of 12 years of research and can be regarded as a standard for the development of knowledge-based systems. It is an excellent basis for the reuse of knowledge and its transfer between expert teams. Although CommonKADS proposes an agent model, it is however not designed for the development of multi-agent systems as such. This shortcoming is resolved by CoMoMAS.KeywordsUnify Modelling LanguageAgent ModelTask ModelExpertise ModelModel TemplateThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Multi-agent systems (MASs) are essential solutions for large-scale, complex problems as single agents are having limited capabilities and computing resources. In real-time problems, the agents have to take dynamic decisions during run-time and coordinate their actions accordingly. In such scenarios, conflicts between agents may arise while coordinating and collaborating dynamic action among them to achieve the goal and even after achieving the goal. To resolve such issues, a model is proposed in this paper which is motivated by the cooperative hunting and feeding behaviour of the crocodiles, i.e., utilises the predatory strategy of crocodiles.

Large language models (LLMs) have advanced geospatial artificial intelligence; however, geospatial knowledge-base question answering (GeoKBQA) remains underdeveloped. Prior systems have relied on handcrafted rules and have omitted the splitting of datasets into training, validation, and test sets, thereby hindering fair evaluation. To address these gaps, we propose a prompt-based multi-agent LLM framework (based on GPT-4o) that translates natural-language questions into executable GeoSPARQL. The architecture comprises an intent analyzer, multi-grained retrievers that ground concepts and properties in the OSM tagging schema and map geospatial relations to the GeoSPARQL/OGC operator inventory, an operator-aware intermediate representation aligned with SPARQL/GeoSPARQL 1.1, and a query generator. Our approach was evaluated on the GeoKBQA test set using 20 few-shot exemplars per agent. It achieved 85.49 EM (GPT-4o) with less supervision than fine-tuned baselines trained on 3574 instances and substantially outperformed a single-agent GPT-4o prompt. Additionally, we evaluated GPT-4o-mini, which achieved 66.74 EM in a multi-agent configuration versus 47.10 EM with a single agent. The observations showed that the multi-agent gain was higher for the larger model. Our results indicate that, beyond scale, the framework’s structure is important; thus, principled agentic decomposition yields a sample-efficient, execution-faithful path beyond template-centric GeoKBQA under a fair, hold-out evaluation protocol.

People can interact much more readily with a multi-agent system if they can understand it in cognitive terms. Modern work on BDI emphasizes explicit representation of cognitive attributes in the design and construction of a single agent, but transferring these concepts to a community is not straightforward. In addition, there are single-agent cases in which this approach cannot yield the desired perspicuity, including fine-grained agents without explicit internal representation of cognitive attributes, and agents whose inner structures are not accessible. We draw together two vintage lines of agent be imputed externally to a system irrespective of its internal structure, and the in-sight from situated automata that dynamical systems offer a well-defined semantics for cognition. We demonstrate this approach in both single agent and multi agent examples.