AUTONOMOUS AGENT FRAMEWORK AND ITS DECISION-MAKING

Autonomy, a key property associated with the agent, is an important topic in the current research of the agent theory. Although no definition of the agent autonomy is universally accepted, an important aspect of the agent autonomy is the decision-making capability of the agents. This paper investigates the autonomy of the agent, presents a framework for autonomous agent and discusses its decision-making process. Started with introducing a language for representing autonomous agent, a framework is proposed for modeling autonomous agent based on a BDI model and the situation calculus. Finally, a kind of decision-making process of the autonomous agent is presented.

Control of group of mobile autonomous agents via local strategies

This paper considers the formation control problem of multi-agent systems in a distributed fashion. Two cases of the information propagating topologies among multiple agents, characterized by graphics model, are considered. One is fixed topology. The other is switching topology which represents the limited and less reliable information exchange. The local formation control strategies established in this paper are based on a simple modification of the existing consensus control strategies. Moreover, some existing convergence conditions are shown to be a special case of our model even in the continuous-time consensus case. Therefore, the results of this paper extend the existing results about the consensus problem.

Development of deep-learning-based autonomous agents for low-speed maneuvering in Unity

This study provides a systematic analysis of the resource-consuming training of deep reinforcement-learning (DRL) agents for simulated low-speed automated driving (AD). In Unity, this study established two case studies: garage parking and navigating an obstacle-dense area. Our analysis involves training a path-planning agent with real-time-only sensor information. This study addresses research questions insufficiently covered in the literature, exploring curriculum learning (CL), agent generalization (knowledge transfer), computation distribution (CPU vs. GPU), and mapless navigation. CL proved necessary for the garage scenario and beneficial for obstacle avoidance. It involved adjustments at different stages, including terminal conditions, environment complexity, and reward function hyperparameters, guided by their evolution in multiple training attempts. Fine-tuning the simulation tick and decision period parameters was crucial for effective training. The abstraction of high-level concepts (e.g., obstacle avoidance) necessitates training the agent in sufficiently complex environments in terms of the number of obstacles. While blogs and forums discuss training machine learning models in Unity, a lack of scientific articles on DRL agents for AD persists. However, since agent development requires considerable training time and difficult procedures, there is a growing need to support such research through scientific means. In addition to our findings, we contribute to the R&D community by providing our environment with open sources.

Build Autonomic Agents with ABLE

The IBM Agent Building and Learning Environment(ABLE) provides a lightweight Java^(TM) agent frame- work,a comprehensive JavaBeansTM library of intelligent software components,a set of development and test tools, and an agent platform.After the introduction to ABLE,classes and interfaces in the ABLE agent framework were put forward.At last an autonomic agent that is an ABLE-based architecture for incrementally building autonomic systems was discussed.

Intelligent Interfaces: Pedagogical Agents and Virtual Humans

Little by little, we are entering the new era, intelligent interfaces are absorbing us more and more every day, and artificial intelligence makes its presence in a stealthy way. Virtual humans that represent an evolution of autonomous virtual agents;they are computer programs and in the future capable of carrying out different activities in certain environments. They will give the illusion of being human;they will have a body, and they will be immersed in an environment. They will have a set of senses that will allow them: 1) Sensations and therefore associated expressions;2) Communication;3) Learning;4) Remembering events, among others. By integrating the above, they will have a personality and autonomy, so they will be able to plan with respect to objectives;allowing them to decide and take actions with their body, in other words, they will count on awareness. The applications will be focused on environments that they will inhabit, or as interfaces that will interact with other systems. The application domains will be multiple;one of them being education. This article shows the design of OANNA like an avatar with the role of pedagogical agent. It was modeled as an affective-cognitive structure related to the teaching-learning process linked to a pedagogical agent that represents the interface of an artilect. OANNA, has the necessary animations for intervention within the teaching-learning process.

DISOPE distributed model predictive control of cascade systems with network communication

A novel distributed model predictive control scheme based on dynamic integrated system optimization and parameter estimation （DISOPE） was proposed for nonlinear cascade systems under network environment. Under the distributed control structure, online optimization of the cascade system was composed of several cascaded agents that can cooperate and exchange information via network communication. By iterating on modified distributed linear optimal control problems on the basis of estimating parameters at every iteration the correct optimal control action of the nonlinear model predictive control problem of the cascade system could be obtained, assuming that the algorithm was convergent. This approach avoids solving the complex nonlinear optimization problem and significantly reduces the computational burden. The simulation results of the fossil fuel power unit are illustrated to verify the effectiveness and practicability of the proposed algorithm.

Evolutionary Computation for Image Feature Extraction

Modifications to an image feature extraction approach involving evolutionary computation and autonomous agents are proposed. The described algorithm allows extraction of features with certain specified characteristics, while omitting other undesirable details in the image. Experimental results are presented with remarks.

Agents learned,but do we?Knowledge discovery using the agent-based double auction markets

This paper demonstrates the potential role of autonomous agents in economic theory.We first dispatch autonomous agents,built by genetic programming,to double auction markets.We then study the bargaining strategies,discovered by them,and from there,an autonomous-agent-inspired economic theory with regard to the optimal procrastination is derived.

Formulating layered adjustable autonomy for unmanned aerial vehicles

Purpose–The purpose of this paper is to propose a layered adjustable autonomy(LAA)as a dynamically adjustable autonomy model for a multi-agent system.It is mainly used to efficiently manage humans’and agents’shared control of autonomous systems and maintain humans’global control over the agents.Design/methodology/approach–The authors apply the LAA model in an agent-based autonomous unmanned aerial vehicle(UAV)system.The UAV system implementation consists of two parts:software and hardware.The software part represents the controller and the cognitive,and the hardware represents the computing machinery and the actuator of the UAV system.The UAV system performs three experimental scenarios of dance,surveillance and search missions.The selected scenarios demonstrate different behaviors in order to create a suitable test plan and ensure significant results.Findings–The results of the UAV system tests prove that segregating the autonomy of a system as multidimensional and adjustable layers enables humans and/or agents to perform actions at convenient autonomy levels.Hence,reducing the adjustable autonomy drawbacks of constraining the autonomy of the agents,increasing humans’workload and exposing the system to disturbances.Originality/value–The application of the LAA model in a UAV manifests the significance of implementing dynamic adjustable autonomy.Assessing the autonomy within three phases of agents run cycle(taskselection,actions-selection and actions-execution)is an original idea that aims to direct agents’autonomy toward performance competency.The agents’abilities are well exploited when an incompetent agent switches with a more competent one.