The network as a whole should exhibit an intelligent and complex behavior emerging from simple node behaviors through interaction. Self-reconfiguration link mobility and node migration. Node and architecture evolution. We will formulate a basic model for adaptive agents, capturing most of the above requirements, as the Interactive Automata Network situated in some environment. In particular, an automaton IA0 represents an environment.
Next the atomic move is repeated any number of times. Note that the model is very general. It is based on random automata networks, which on other hand have been derived from cellular automata, i. The graph and the nodes in IAN can be arbitrary. The environment is modeled uniformly as an interactive automaton, which can be a nondeterministic or stochastic type assuming our incomplete knowledge about the environment. A distributed environment, if it was required, could be modeled as a subnetwork, instead of a single node.
Please keep in mind that we strive for simplicity, knowing that additional requirements will sufficiently complicate our simple model. Additional requirements include asynchronous work, selfreconfiguration of the network link and node mobility , learning new transition rules, and optimization adaptation , in general. Emerging Behavior and Other Properties of Autonomous Agents Interactive automata can be viewed as a dynamical system of a discrete type, where the local dynamics local transition rules induce global dynamics, i. As a dynamical system, the most basic question about a global map T is the effect of its repeated application in a phase space to a random given configuration x.
Now we are ready to define formally several important properties of autonomous agents. All nodes communicate by message-passing through sensor and effectors. The input and output messages consist of orders sonar or radio , reports data or status , and sensory data from and to environment. M is a finite set of tasks currently 3 implemented: an initial hierarchy building, search, and mission termination ,?
Simply, an ARL intelligent controller has itself been implemented as a three-level hierarchy: tasks consisting of behaviors, and behaviors consisting of atomic actions. A hierarchical structure allows to hide complexity, to improve reliability by graceful degradation, to increase the speed of adaptation by decreasing the search space, and to increase the speed of execution by employing parallelism.
However, we should bear in mind that the advantage of the hierachical structure over a nonhierachical one depends on many factors, and the optimal structure of the tree its height and span requires proper analysis for specific tasks. The best idea would be if such an architecture could evolve. For the more complicated tasks, the pure hierarchical structure may not suffice. This can be established by mobile communication links which will be added temporarily for the duration of task. To achieve a common goal, the group of cooperating automata may use some kind of perfomance metric providing feedback and allowing to achieve the objectives by minimizing the perfomance objective function.
But the above requirements lead us to the natural extension of the IAN model by process algebras.
Process Algebra Extension to the Basic Model The basic IAN model does not capture asynchronous message-passing, network reconfiguration, optimization and adaptation. To deal with asynchrony, the basic model could be extended using Petri nets with tokens interpreted as predicates: message present or not. However, this would not be sufficient to capture reconfiguration and evolution. Petri nets use static interconnection topology between entities, and agents require dynamic topologies.
Process algebras see e.https://viptarif.ru/wp-content/phone/2541.php
Springerbriefs In Cooperating Objects series - dramboholpartllem.cf
Its basic element is functions with operator part and arguments. Some cost functions are built-in in the calculus e. However, existing AUVs e. It is perhaps too early to enforce a single standard in the AUV design. The only reasonable way to allow collaboration of such heterogeneous AUVs is to agree and implement a common language for communication, called here a Generic Behavior Message-Passing Language.
The collaborative mission execution infrastructure is presented in Fig. The general idea is based on the network of cooperating interactive automata, which communicate by send and receive primitives.
A Remote Integrated Testbed for Cooperating Objects
This means that we can build a mission as a program consisting of elementary behaviors. It is up to a specific implementation whether those behaviors will be decomposed into lowerlevel actions, or not. We assume that nodes vehicles currently communicate using own formats and types of messages, thus wrappers will be needed to messages already in the standard form.
If the language is found to be useful and flexible enough, it is expected that future controllers will produce messages directly in the standard form. Thus wrappers can and should be eliminated as the long-term goal. Virtual Controller from Fig. The requirements for the generic behavior message-passing language include the following:? All behaviors are defined as functions and are communicated between nodes as messages by send and receive primitives. The generic behavior language consist of two groups of behaviors:?
All behaviors are communicated through two predefined elementary behaviors: send and receive.
Archived — The Digital Integration/Cooperation Challenge
Send and receive always work in pairs. For communication, send and receive should use the same name of communication link. However, very few books devoted to testbeds have been published. To the best of my knowledge no book on this topic has been published. This book is particularly interesting for the growing community of testbed developers. I believe the book is also very interesting for researchers in robot-WSN cooperation.
This book provides detailed description of a system that can be considered the first testbed that allows full peer-to-peer interoperability between heterogeneous robots and ubiquitous systems such as Wireless Sensor Networks, camera networks and pervasive computing systems. The system architecture includes modules that allows full bidirectional communication between robots and WSN. One robot can. The book describes the current state of the art in development of testbeds integrating Cooperating Object technologies. It describes in detail the testbed specification and design using requirements extracted from surveys among experts in robotics and ubiquitous systems in academia and industry.
A Remote Integrated Testbed for Cooperating Objects
From that point on they could practice for the individual execution of the task if they wished so. It is important to mention that individual execution does not mean that only one of the users could manipulate the object during a task. In fact, both could do it, but never simultaneously on the same object. At this time, the users were requested to develop a strategy for performing the task together.
The users were encouraged to talk by using, whenever possible, the elements from the virtual environment itself, in order to demonstrate their ideas, strategies or intentions.
The goal of this approach was to enhance even more the level of knowledge about the virtual environment and the users' feeling of presence in that environment. Then, I will manage to adjust it". Those sentences were invariably followed by the indication of the object through the use of the user's pointer. The virtual environment presented to the users in that step was the same that would later be used for the actual experiments. V Tests using the individual interaction technique: after the training session, the users were again "inserted" in the virtual environment and the task to be done was presented once again.
The task performance was then timed. It is worth pointing out that the task execution was done in a collaborative way, but not simultaneously. The trial ended when a certain level of accuracy of object positioning and orientation was achieved. After completing this phase with the noncooperative interaction technique, the users were asked to remove their glasses and to answer the first three parts of the evaluation questionnaire. VI Training for the collaborative technique: at this moment, the cooperative interaction technique was presented to the users.
They could then test it for as long as they needed in order to feel comfortable with its use. The users were first requested to develop a strategy to perform the task together. In order to make the evaluation simpler, the users were asked to use the cooperative manipulation as much as possible. VII Tests using the cooperative metaphor: the users did their tasks in the cooperative way having their performance time measured once more. It is important to note that the configuration of the virtual environment the initial position of the objects and the users for this phase was the same as the one used for individual technique in the beginning of the manipulation phase using the individual technique.
After finishing the task with the cooperative interaction technique the users were requested to take off their virtual reality glasses and to answer the rest of the evaluation questionnaire. VIII At the end of the experiment a quick informal interview was conducted with the users in order to find out if they had felt any kind of discomfort during the experiment, or if there was any additional comment they would like to make.
In the next three sections we provide a detailed description of each experiment. The first VE was designed with the purpose of evaluating the effect of cooperative techniques in the performance of users in tasks that required adjusting the position and orientation of objects. The VE simulates a classroom in which two users in opposite corners of the room have to place computers on the desks. Figure 14 shows the view of one of the users. The task was to place four computers on four desks in the middle of the room, in such a way that the computers had their screens facing the opposite side of the white board.