Intelligent Agent Model Research Articles

Towards an Affective Intelligent Agent Model for Extrinsic Emotion Regulation

Emotion regulation is the human ability to modulate one’s or other emotions to maintain emotional well-being. Despite its importance, only a few computational models have been proposed for facilitating emotion regulation. None of them prepare a plan of all the actions necessary for emotion regulation customized to the needs of a specific individual. To address this gap, we propose a computational model for an intelligent agent which, grounded in a multidimensional emotion representation, facilitates emotion regulation in individuals. This computational model is based on J. Gross’s theoretical framework of emotion regulation. An intelligent agent selects the most appropriate regulation strategy to maintain an individual’s emotional equilibrium considering the individual’s personality traits. A dynamic planner prepares a plan of emotion regulation actions which is dynamically adapted according to the emotional changes observed in the individual after applying the previous emotion regulation actions. This refinement of the initial regulatory action plan allows the proposed emotion regulation agent to adapt the plan to the specific characteristics of the individual, facilitating the individual to improve their emotion regulation capabilities and improve their emotional health.

Intelligent Optimization of Gas Flooding Based on Multi-Objective Approach for Efficient Reservoir Management

The efficient development of oil reservoirs mainly depends on the comprehensive optimization of the subsurface fluid flow process. As an intelligent analysis technique, artificial intelligence provides a novel solution to multi-objective optimization (MOO) problems. In this study, an intelligent agent model based on the Transformer framework with the assistance of the multi-objective particle swarm optimization (MOPSO) algorithm has been utilized to optimize the gas flooding injection–production parameters in a well pattern in the Middle East. Firstly, 10 types of surveillance data covering 12 years from the target reservoir were gathered to provide a data foundation for model training and analysis. The prediction performance of the Transformer model reflected its higher accuracy compared to traditional reservoir numerical simulation (RNS) and other intelligent methods. The production prediction results based on the Transformer model were 21, 12, and 4 percentage points higher than those of RNS, bagging, and the bi-directional gated recurrent unit (Bi-GRU) in terms of accuracy, and it showed similar trends in the gas–oil ratio (GOR) prediction results. Secondly, the Pareto-based MOPSO algorithm was utilized to fulfil the two contradictory objectives of maximizing oil production and minimizing GOR simultaneously. After 10,000 iterations, the optimal injection–production parameters were proposed based on the generated Pareto frontier. To validate the feasibility and superiority of the developed approach, the development effects of three injection–production schemes were predicted in the intelligent agent model. In the next 400 days of production, the cumulative oil production increased by 25.3% compared to the average distribution method and 12.7% compared to the reservoir engineering method, while GOR was reduced by 27.1% and 15.3%, respectively. The results show that MOPSO results in a strategy that more appropriately optimizes oil production and GOR compared to some previous efforts published in the literature. The injection–production parameter optimization method based on the intelligent agent model and MOPSO algorithm can help decision makers to update the conservative development strategy and improve the development effect.

Current trend in control of artificial intelligence for health robotic manipulator

The increasing utilization of artificial intelligence and robots in various services in healthcare makes robots as preferred intelligent agent model. Robotic evolution produces the optimal robotic innovation in the robotic system or its subsystems, morphology, kinematics, and control. An intelligent algorithm is programmed into the control of the robotic manipulator. This paper aims to identify the control of artificial intelligence and identify comparisons of artificial intelligence algorithms control for healthcare robotic manipulators. This study uses a systematic literature review using the Preferred Reporting Items for Systematic Review (PRISMA). The potential for further articles is explored related to the theme of the research carried out. The conclusion obtained many studies have been carried out to optimize the work and tasks of the robotic arm manipulator, specifically developing various types of manipulator control (algorithms) combined with neural networks to get the right and appropriate algorithm.

Stress prediction of bridges using ANSYS soft and general regression neural network

Bridge managers are often eager to identify the carrying capacities and service performance characteristics of bridge structures. In view of the massive number of bridges managed and maintained in China, providing a quick evaluation of bridge performance bridges has become an urgent challenge for bridge engineering. Aiming at many adverse problems of bridge load tests, such as their high costs, long time consumption, low efficiency, impact on traffic, and damage to bridge structures, a new method for bridge static behavior prediction is proposed herein, based on dynamic load test data. The method adopts a generalized regression neural network algorithm with fast convergence and a nonlinear approximation, determines the sensitive parameters based on a full-bridge sensitivity analysis, forms training samples using a uniform design sampling method, and establishes intelligent agent model. The initial finite element analysis model of the bridge structure is updated using the proposed proxy model and dynamic load test data, and the static behavior of the existing bridge structure is accurately predicted based on the updated finite element model. To verify the correctness of the proposed method, a practical bridge is considered as the research object, and the static response of the bridge structure is predicted. The predicted results are consistent with the static load test results for the bridge, verifying the correctness of the proposed method. The results show that the maximum error between the theoretical calculation result and measured value is only 4.34% and the minimum error is only 3.52%. The strain prediction results are consistent with the measured results, and the modified theoretical analysis model provides high accuracy for strain prediction results under different working conditions. The application in practical engineering shows that the prediction results under different load conditions are adaptable and highly accurate.

Can Agents Model Hydrocarbon Migration for Petroleum System Analysis? A Fast Screening Tool to De-Risk Hydrocarbon Prospects

Understanding subsurface hydrocarbon migration is a crucial task for petroleum geoscientists. Hydrocarbons are released from deeply buried and heated source rocks, such as shales with a high organic content. They then migrate upwards through the overlying lithologies. Some hydrocarbon becomes trapped in suitable geological structures that, over a geological timescale, produce viable hydrocarbon reservoirs. This work investigates how intelligent agent models can mimic these complex natural subsurface processes and account for geological uncertainty. Physics-based approaches are commonly used in petroleum system modelling and flow simulation software to identify migration pathways from source rocks to traps. However, the problem with these simulations is that they are computationally demanding, making them infeasible for extensive uncertainty quantification. In this work, we present a novel dynamic screening tool for secondary hydrocarbon migration that relies on agent-based modelling. It is fast and is therefore suitable for uncertainty quantification, before using petroleum system modelling software for a more accurate evaluation of migration scenarios. We first illustrate how interacting but independent agents can mimic the movement of hydrocarbon molecules using a few simple rules by focusing on the main drivers of migration: buoyancy and capillary forces. Then, using a synthetic case study, we validate the usefulness of the agent modelling approach to quantify the impact of geological parameter uncertainty (e.g., fault transmissibility, source rock location, expulsion rate) on potential hydrocarbon accumulations and migrations pathways, an essential task to enable quick de-risking of a likely prospect.

From Affect Theoretical Foundations to Computational Models of Intelligent Affective Agents

The links between emotions and rationality have been extensively studied and discussed. Several computational approaches have also been proposed to model these links. However, is it possible to build generic computational approaches and languages so that they can be “adapted” when a specific affective phenomenon is being modeled? Would these approaches be sufficiently and properly grounded? In this work, we want to provide the means for the development of these generic approaches and languages by making a horizontal analysis inspired by philosophical and psychological theories of the main affective phenomena that are traditionally studied. Unfortunately, not all the affective theories can be adapted to be used in computational models; therefore, it is necessary to perform an analysis of the most suitable theories. In this analysis, we identify and classify the main processes and concepts which can be used in a generic affective computational model, and we propose a theoretical framework that includes all these processes and concepts that a model of an affective agent with practical reasoning could use. Our generic theoretical framework supports incremental research whereby future proposals can improve previous ones. This framework also supports the evaluation of the coverage of current computational approaches according to the processes that are modeled and according to the integration of practical reasoning and affect-related issues. This framework is being used in the development of the GenIA3 architecture.

An Intelligent Agent Model and a Simulation for a Given Task in a Specific Environment

This paper introduces An Intelligent Agent Model for a Given Task in a Specified Environment. The methodology adopted in this work is based on mixing computational methods and functions to build an intelligent agent model. This paper focuses on building an intelligent agent model as a knowledge-based system that interacts with a dynamic environment for performing tasks. The class structure used to represent the environment in the knowledge base relies on three types of knowledge representation forms: production rule, semantic net, and frames. Each object in the environment is an instance of the class environment. Algorithms and functions are used to get knowledge from the state space of an environment to construct a task. The intelligent agent model can understand the environment from any position and can detect many subtasks, arrange them in a queue for execution, and can make decisions at a high scale of thinking. This model is proposed to maintain that an agent which is characterized by sufficiently low computational costs can interact with the environment in real-time but is powerful enough to reach the assigned goals in complex environments and within an acceptable time period. The intelligent agent model can calculate persistent changes in an external dynamic environment and any unexpected change, for example detecting the being of any problem in the environment and avoiding it. The intelligent agent can also learn and take reasonable decisions in the dynamic environment and automatically select an action based on task features. Thus, the intelligent agent can resolve several different kinds of difficulties.

A Brain-Inspired Adaptive Space Representation Model Based on Grid Cells and Place Cells.

Grid cells and place cells are important neurons in the animal brain. The information transmission between them provides the basis for the spatial representation and navigation of animals and also provides reference for the research on the autonomous navigation mechanism of intelligent agents. Grid cells are important information source of place cells. The supervised learning and unsupervised learning models can be used to simulate the generation of place cells from grid cell inputs. However, the existing models preset the firing characteristics of grid cell. In this paper, we propose a united generation model of grid cells and place cells. First, the visual place cells with nonuniform distribution generate the visual grid cells with regional firing field through feedforward network. Second, the visual grid cells and the self-motion information generate the united grid cells whose firing fields extend to the whole space through genetic algorithm. Finally, the visual place cells and the united grid cells generate the united place cells with uniform distribution through supervised fuzzy adaptive resonance theory (ART) network. Simulation results show that this model has stronger environmental adaptability and can provide reference for the research on spatial representation model and brain-inspired navigation mechanism of intelligent agents under the condition of nonuniform environmental information.

Інтелектуальна навчальна система підготовки диспетчерів управління повітряним рухом

В статті розглянуті основні методи організації інтелектуального навчального середовища підготовки диспетчерів управління повітряним рухом. Обґрунтована необхідність створення інтелектуальної навчальної системи в адаптивних тренажерах диспетчерів управління повітряним рухом. Інтелектуальні навчальні системи повинні базуватись на основі синтезу імітаційно-моделюючих комплексів у вигляді розподілених систем обробки даних для імітації поведінки середовища навчання. В роботі сформовані вимоги, що висуваються до побудови мультиагентного середовища інтелектуальної навчальної системи підготовки диспетчерів управління повітряним рухом. З метою забезпечення моделювання інтелектуального поводження об'єктів, що входять у віртуальне середовище навчання, запропоновано створення інтелектуальних об'єктів, як елементів мультиагентної системи з використанням методів планування дій. Запропоновано підхід удосконалення та розширення функціональних можливостей системи підготовки диспетчерів управління повітряним рухом. Запропонована архітектура інтелектуального агента навчальної системи підготовки диспетчерів управління повітряним рухом створена на базі елементів InterRRa архітектури, що забезпечує взаємодію агента з зовнішнім середовищем та іншими агентами через модель фізичного представлення об'єкта. Наведена математична модель інтелектуального агента в якій враховано можливість здійснення впливу на зовнішнє середовище. Розроблена модель мультиагентного середовища інтелектуальної навчальної системи яка забезпечує ідентифікацію ситуації в підсистемі підготовки і прийняття рішень, що виконує передачу управління на відповідний рівень ієрархії системи поводження інтелектуального агента. Особливістю розробленої структури мультиагентного середовища інтелектуальної навчальної системи є використання моделі поведінки інтелектуальних агентів, що забезпечують змінну поведінку і можливість рішення задач підготовки і прийняття рішень своїх подальших дій за допомогою різних моделей поведінки.

An intelligent agent model for learning group development in the digital learning environment: A systematic literature review

Group development is the first and most important step for the success of collaborative problem solving (CPS) learning in the digital learning environment (DLE). A literacy study is needed for studies in the intelligent agent domain for group development of collaborative learning in DLE. This paper is a systematic literature review (SLR) of intelligent agents for group formation from 2001 to 2019. This paper aims to find answers to 4 (four) research questions, namely: 1) What components to develop intelligent agents for group development; 2) What is the intelligent agent model for group development; 3) How are the metrics for measuring intelligent agent performance; and 4) How is the Framework for developing intelligent agent. The components of the intelligent agent model consist of: member attributes, group attributes (group constraints), and intelligent techniques. This research refers to Srba and Bielikova's group development model. The stages of the model are formation, performing and closing. An intelligent agent model at the formation stage. A performance metric for the intelligent agent at the performance stage. The framework for developing an intelligent agent is a reference to the stages of development, component selection techniques, and performance measurement of an intelligent agent.

A conceptual model of communication for intelligent agents in the infrastructure of smart transportation systems

In this article, at a conceptual level, the issues of data exchange between objects of a complex cyber-physical system using a multi-agent approach are considered. A multi-agent communication model of intelligent agents is described, which is the interaction of participants in the Smart Transportation information management space. Unmanned vehicles, pedestrian mobile devices, smart traffic lights, traffic signs and other infrastructure of the Smart City are considered as model agents.

Computer multi-agent model of chemico-physiological processes in the human gastrointestinal tract as a living biochemical system

A methodology was proposed to develop an original computer-aided multiagent model of chemicophysiological processes in the human gastrointestinal tract (GIT) as a complex living biochemical system (LBCS), which simulates chemicophysiological processes of interaction of biologically active parts and organs of GIT in models of intelligent organoid agents. The model vividly represents chemicophysiological processes of the complex LBCS as a whole and also predicts its viability and homeostasis in cases of various physiological and biochemical external actions, internal abnormalities, and malfunctioning of GIT organs.

A simheuristic approach for evolving agent behaviour in the exploration for novel combat tactics

The automatic generation of behavioural models for intelligent agents in military simulation and experimentation remains a challenge. Genetic Algorithms are a global optimization approach which is suitable for addressing complex problems where locating the global optimum is a difficult task. Unlike traditional optimisation techniques such as hill-climbing or derivatives-based methods, Genetic Algorithms are robust for addressing highly multi-modal and discontinuous search landscapes. In this paper, we outline a simheuristic GA-based approach for automatic generation of finite state machine based behavioural models of intelligent agents, where the aim is the identification of novel combat tactics. Rather than evolving states, the proposed approach evolves a sequence of transitions. We also discuss workable starting points for the use of Genetic Algorithms for such scenarios, shedding some light on the associated design and implementation difficulties.

Bidding Simulation Methods of Multi-input Decision Factors for Power Suppliers based on Intelligent Agent

With the development of electric power industry, it has become an urgent problem of deregulation of the industry. From monopoly to free competition, the market participants’ bidding decisions will undergo great change. Because of difference of market participants’ cost, risk preference, relationship of supply and demand, bidding behaviors of market participants show a more perplexing situation. In this paper, a multi-input decision factors algorithm based on intelligent agent is used to simulate the behavior of generators. The influence of subordinate objective of decision and risk preference on the bidding behavior of generators is considered in the model. The case analysis shows that generator's intelligent agent model established in this paper can well simulate generators of different characteristics; through learning the historical experience, generators can improve their bidding behaviors, update the selection probability of each bidding strategy, and finally achieve good returns.

Benchmarking Projective Simulation in Navigation Problems

Projective simulation (PS) is a model for intelligent agents with a deliberation capacity that is based on episodic memory. The model has been shown to provide a flexible framework for constructing reinforcement-learning agents, and it allows for quantum mechanical generalization, which leads to a speed-up in deliberation time. PS agents have been applied successfully in the context of complex skill learning in robotics, and in the design of state-of-the-art quantum experiments. In this paper, we study the performance of projective simulation in two benchmarking problems in navigation, namely the grid world and the mountain car problem. The performance of PS is compared to standard tabular reinforcement learning approaches, Q-learning and SARSA. Our comparison demonstrates that the performance of PS and standard learning approaches are qualitatively and quantitatively similar, while it is much easier to choose optimal model parameters in case of projective simulation, with a reduced computational effort of one to two orders of magnitude. Our results show that the projective simulation model stands out for its simplicity in terms of the number of model parameters, which makes it simple to set up the learning agent in unknown task environments.

Unified behavior framework in discrete event simulation systems

Intelligent agents provide simulations a means to add lifelike behavior in place of manned entities. When implemented, typically a single intelligent agent model (or approach to defining decision making), such as rule-based, behavior trees, neural networks, etc., is selected. This choice introduces restrictions into what behaviors agents can manifest, and can require significant testing in edge cases. This paper presents the incorporation and application of the Unified Behavior Framework (UBF) into the Advanced Framework for Simulation, Integration, and Modeling environment. The UBF provides the flexibility to implement any and all behavior-based systems, allowing the developer to rapidly assemble a decision making agent that leverages multiple paradigms or approaches. The UBF achieves this by leveraging several key software engineering principles: modular design, scalability through reduced code complexity, simplified development and testing through abstraction, and the promotion of code reuse. The use of UBF to define intelligent agents within a 2v2 Integrated Air Defense System is demonstrated.

Models of Neuro-fuzzy Agents in Intelligent Environments

The set of agent-oriented information systems that interact in a changing structure and operation conditions, is considered as a distributed intellectual environment. Such a representation is corresponding in geographically distributed systems (region, city), corporate information systems of enterprises and organizations, social groups within the various territorial structures, distributed heterogeneous systems and networks territorial entities, enterprises and organizations. On the basis of known models of neuro-fuzzy agents are proposed a model of the fundamental intelligent agent (FIA), operating in a dynamic heterogeneous information environment and carrying out reception of input messages in a usual source language and fuzzy input language, the creation of messages in the inner language to perform reasoning and reflection which can be either conventional or fuzzy, and transmitting to the external environment accurate and fuzzy messages in appropriate languages. A classification of possible FIA models are constructed in distributed intelligent systems and environments. A behaviour model of the FIA, defined Post calculus axiom schemes, combining precise and fuzzy representation of variables and manipulate the internal states, base attributes and logical formulas in fuzzy logic and first-order predicate calculus are constructed.

Application and Research on The Model of Distributed Intelligent Cooperative Decision Support System

Combined with distributed processing, traditional decision support and collaborative technology, an intelligent distributed collaborative decision support system model is proposed. The architecture, component and functions of the model is described in detail .Analyzed the problem solving process of distributed collaborative decision problem and intelligent collaborative decision-making model is proposed. Finally the intelligent agent model of the system and its application technology is given, which laid the foundation of this new model and its application in military decision making system.

Agent reactive capabilities in dynamic environments

Currently, there are many intelligent agent models that provide a number of components or abstractions to solve different types of problems. However, few of them provide abstractions or concepts that allow considering how to deal with complex dynamic environments (an environment that changes continuously in terms of available resources, global behavioral rules, etc.). In this work, we propose two abstractions that provide the developer with a new way of modeling reactive agent capabilities in dynamic environments. The first abstraction focuses on how to process the environmental stimuli as events; the second abstraction specifies how to launch tasks in response to events, which is an approach that is based on event–condition–action rules. Moreover, we present an example that is based on a call center CBR-based application, and a performance evaluation of the proposal is also provided.

Tailoring a cognitive model for situation awareness using machine learning

Using a pure machine learning approach to enable the generation of behavior for agents in serious gaming applications can be problematic, because such applications often require human-like behavior for agents that interact with human players. Such human-like behavior is not guaranteed with e.g. basic reinforcement learning schemes. Cognitive models can be very useful to establish human-like behavior in an agent. However, they require ample domain knowledge that might be difficult to obtain. In this paper, a cognitive model is taken as a basis, and the addition of scenario specific information is for a large part automated by means of machine learning techniques. The performance of the approach of automatically adding scenario specific information is rigorously evaluated using a case study in the domain of fighter air combat. An evolutionary algorithm is proposed for automatically tailoring a cognitive model for situation awareness of fighter pilots. The standard algorithm and several extensions are evaluated with respect to performance in air combat. The results show that it is possible to apply the algorithm to optimize belief networks for cognitive models of intelligent agents (adversarial fighters) in the aforementioned domain, thereby reducing the effort required to elicit knowledge from experts, while retaining the required `human-like' behavior.