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Figure 2. Screen capture of the current application-INTELLIGENT AGENT FOR ACQUISITION OF THE MOTHER TONGUE VOCABULARY

From this screen capture (figure 2), you can observe that, for example, the noun car (masina) is in a great correspondence with the correct word car(masina), and in the same correspondence with the incorrect word small (mic). But this is due to the fact that the system worked only with few examples. After a training with much more examples, the system will increase the correspondence between the concept/object car and the word car, and the correspondence between the object car and the word little will remain smallest.

Figure 1. Associations between objects and words-Intelligent Agent for Acquisition of the Mother Tongue Vocabulary

So, the learning process is very complex, and it is a probabilistic one. Thus, a second example is required, and even more than that. Normally, the mother speaks naturally to her child, she speaks with love and affection, she doesn’t “judge” or “program” what to say to her child. Therefore, another day she will tell her child, for example, “My darling son, let’s drink the milk from the cup.”

Figure 3. The multi-agent system-Optimization of Distributed Systems Using Multi-Agent Systems with Virtual Time

The authority is given to a single agent that is symbolically situated on the top level; this agent would be considered the root of an arborescent structure; but the stages of the process are provided by groups of agents, respectively by the relations of communication between them. Thus, we would deal with a ZERO AGENT and several groups of agents. Such a group is made up of several agents, each and one of these agents accomplishing a certain role in the process of synchronization (figure 3).

Figure 2. Lamport's algorithm-Optimization of Distributed Systems Using Multi-Agent Systems with Virtual Time

In order to synchronize logical clocks, Lamport [3] defined the relationship “happened before” (preceded) which implies that the expression 1 2 a → a means “ 1 a occurred before 2 a ”, and it means that all the processes coincide in the fact that 1 a took place first, and subsequently 2 a took place. This relation can be directly observed in two situations (figure 2): 1. If two events happen during the same process, the order of the happening is indicated by the common clock; 2. When two processes communicate through a message, the event that corresponds to sending the precise message always happens before the event of receiving it (i.e. the message).

Figure 1. Cristian's Algorithm-Optimization of Distributed Systems Using Multi-Agent Systems with Virtual Time

Cristian’s Algorithm (figure 1) is a method for clock synchronization which can be used in many fields of distributive computer science. It suffers, though, in implementations using a single server, making it unsuitable for many distributive applications where redundancy may be crucial.

Table 1. The declination of feminine nouns (after [4])-ADX — Agent for Morphologic Analysis of Lexical Entries in a Dictionary

An example of such searching trees for nouns of all genders (masculine, feminine, neuter) together with the entire analysis made by the ADX agent for the word “cepelor” is presented in figure 2. The pairs of numbers in brackets form lists of lines and columns from the inflections’ charts where endings from the top of the list are found. We have noted the zero ending with – (that is always the root of the tree) and the void list with * (Null/Nil).

opencc-by-4.0Oct 2010View details →

Figure 1. Affordance in Relation with Environment and Agent

In figure 1, relevance emerges in a third dimension, as a result of the interaction between perception/activity (through affordance and agent /environment relationship; environment is full of meaning potential; the agent has certain abilities; aptitude, effectiveness, fitness, or whatever psychologists, biologists or anthropologists might call it; affordance fuels perception and activity, and brings about meaning.

opencc-by-4.0Nov 2011View details →

Figure 7. The outcomes of the Monitoring and Control of the Greenhouse soil and climate Conditions for tomato crops-Design and Development a Control and Monitoring System for Greenhouse Conditions Based-On Multi Agent System

In the past generation greenhouses it was enough to have one cabled measurement point in the middle to provide the information to the greenhouse automation system. The system itself was usually simple without opportunities to control locally heating, lights, ventilation or some other activity, which was affecting the greenhouse interior climate. The optimal greenhouse climate and soil adjustment can enable us to improve productivity and to achieve remarkable energy savings. In this paper we proposed a multi-agent methodology for integrated management systems in greenhouses. In this regards wireless sensor networks play a vital role to monitor greenhouse and environment parameters. Each controlled process of the greenhouse environment is modeled as an autonomous agent with its own inputs, its own outputs and its own interactions with the other agents. Each agent acts autonomously, as it knows a priori the desired environmental set-points. In this way, any possible conflicting decisions of conventional environmental control methodologies are resolved through negotiations between the agents so that the possible optimal integrated solution is achieved. The developed system is simple, cost effective, and easily installable.

Figure 6. Structure of the MAS for integrated greenhouse management system.-Design and Development a Control and Monitoring System for Greenhouse Conditions Based-On Multi Agent System

The complete agents “environment” is shown in figure 6. Each agent receives the necessary environmental or plant condition measurements. While each agent keeps its autonomy and has its own “personal” goals, it interacts with the other agents through some specific communication language, in the “Agent discussion area”, where the overall goal is taken into account. This interaction takes place under the presence of information from the existing models and the target goals. At that point, each agent has decided on its best strategy.

Figure 2. Experimental wireless sensors setup in greenhouse-Design and Development a Control and Monitoring System for Greenhouse Conditions Based-On Multi Agent System

Figure 2 illustrates how the sensor nodes were deployed to the greenhouse block. The idea of the vertical deployment was to get a better understanding of the microclimate layers which typically exist in the greenhouse, and to figure out what kind of differences occur in the climate between lower and upper flora.

Figure 5. Multi-agent system information and knowledge scheme.-Design and Development a Control and Monitoring System for Greenhouse Conditions Based-On Multi Agent System

The negotiations between agents are subject to optimization based on “knowledge” that is derived from complete production models, yield models or even sparse models as expressed in fuzzy expert rules or practical rules of thumb. In addition, pest control and plant disease models provide additional information useful to the design of a successful strategy for optimal management [15] (illustrated in Figure 5).

Figure 1. Agent Architecture-Design and Development a Control and Monitoring System for Greenhouse Conditions Based-On Multi Agent System

The multi agent method creates a non-structured environment using agents, these agents, in order to reach the global optimum, must be capable of performing transactions between each other, achieving several “deals”, which is performed though a common vocabulary, a finite set of information exchange, a finite set of possible actions, penalties, etc. The power of each agent depends on the degree of contribution of its represented process to the final output of the entire system. Each agent communicates with the environment and adapts to its own internal state as well as to the state of the entire system. In this systems, for the realization of the multi agent architecture the JADE 4.1.1 (Java Agent DEvelopment Framework) have been used [7].

Figure 4. The multi-agent overall environment for integrated management-Design and Development a Control and Monitoring System for Greenhouse Conditions Based-On Multi Agent System

The value of the final product incorporates not only quantity issues but also quality issues, which are difficult to be measured or even estimated. The environment of each agent is defined by the same parameters that define the physical environment in addition to internal states reported by each agent.

Figure 3. Photosynthetic activity in different wavelengths of light radiation. 5.-Design and Development a Control and Monitoring System for Greenhouse Conditions Based-On Multi Agent System

The greenhouse protects the plants from the extreme weather conditions. However, if the period of daylight prevents the photosynthetic activity, the plants do not grow. Horticultural lighting allows the grower to extend the growing season. It enables a year-round producing of plants or makes it possible for the grower to start sowing in early spring and continue season till the first frost. Plants need about 10-12 hours light to improve growth. When the plants are producing flowers or fruits the supplemental need of light per day increases up to 16 hours. Figure 3 shows the photosynthetic activity in different wavelengths of light radiation [16].

openMar 2023View details →

The Role of Lexical Alignment in Human Understanding of Explanations by Conversational Agents

This dataset was collected as part of the research investigating the role of lexical alignment in understanding. The dataset consists of the responses of 179 participants recruited via Prolific (a crowdsourcing platform). 1. Dialogue with a conversational agent (CA) 2. Survey questionnaire responses from the participants. 3. Primes selected or provided by the participants.   This repository contains: <ol> <li>Cleaned data sources listed above. The data is cleaned by removing data from participants who failed attention checks, or any other factor leading to error such as experiment setup, etc.</li> <li>A Python (Jupyter) playbook to analyse the collected data (Analysis_20230927.ipynb)</li> <li>Experiment design (exp_design_flow.png)</li> </ol>

opencc-by-4.0Dec 2018View details →

Fig. 1 in Long-term exposure of Aedes aegypti to Bacillus thuringiensis svar. israelensis did not involve altered susceptibility to this microbial larvicide or to other control agents

Fig. 1 Resistance ratios (RR) betseen the lethal concentrations of Bti and its toxins (Cru11Aa, Cru4Ba), temephos (Tem) and diflubenzuron (Dif) for third-instar Ae. aegypti larvae from the RecBti strain compared to that of the reference strain. a RR at LC50. b RR at LC90

opencc-by-4.0Apr 2024View details →

Dataset for "Adaptive connectivity control in networked multi-agent systems: A distributed approach"

<div> <div>Dataset accompanying the paper "Adaptive connectivity control in networked multi-agent systems: A distributed approach" by M. Krizmancic and S. Bogdan submitted to PLOS ONE journal on April 30, 2024.</div> <div> </div> <div> <div> <div>Contains:</div> <ul> <li>Vector images of the figures presented in the paper.</li> <li>Data files containing the values used to build the figures.</li> </ul> Detailed information and instructions are available in the README file within the dataset. </div> </div> </div>

dryad40/100

Supplementary data for: Comparison of transcriptomic profiles between HFPO-DA and prototypical PPARa, PPARg, and cytotoxic agents in mouse, rat, and pooled human hepatocytes

Like many per- or polyfluorinated alkyl substances (PFAS), toxicity studies with HFPO-DA (ammonium,2,3,3,3-tetrafluoro-2-(heptafluoropropoxy)-propanoate), a short-chain PFAS used in the manufacture of some types of fluorinated polymers, indicate that the liver is the primary target of toxicity in rodents following oral exposure. Although the current weight of evidence supports the PPARa mode of action (MOA) for liver effects in HFPO-DA-exposed mice, alternate MOAs have also been hypothesized including PPARg or cytotoxicity. To further evaluate the MOA for HFPO-DA in rodent liver, transcriptomic analyses were conducted on samples from primary mouse, rat and pooled human hepatocytes treated for 12, 24 or 72 hours with various concentrations of HFPO-DA, or agonists of PPARa (GW7647), PPARg (rosiglitazone), or cytotoxic agents (i.e., acetaminophen or d-galactosamine). Concordance analyses of enriched pathways across chemicals within each species demonstrated greatest concordance between HFPO-DA and PPARa agonist GW7647-treated hepatocytes compared to the other chemicals evaluated. These findings were supported by benchmark concentration modeling and predicted upstream regulator results. In addition, transcriptomic analyses across species demonstrated a greater transcriptomic response in rodent hepatocytes treated with HFPO-DA or agonists of PPARa or PPARg, indicating rodent hepatocytes are more sensitive to HFPO-DA or PPARa/g agonist treatment. These results are consistent with previously published transcriptomic analyses and further support that liver effects in HFPO-DA-exposed rodents are mediated through rodent-specific PPARa signaling mechanisms as part of the MOA for PPARa activator-induced rodent hepatocarcinogenesis. Thus, effects observed in mouse liver are not appropriate endpoints for toxicity value development for HFPO-DA in human health risk assessment.

opencc-zeroMay 2024View details →

opencc-by-4.0Apr 2024View details →

[SELFY] Multi-Agent Situational Awareness Database

This dataset has been created in the context of SELFY project. It includes ROS1 rosbags comprising both real-world measurements and synthetic data generated with the CARLA simulator, as well as the definitions of ROS messages. Each rosbag contains the following type of data: <ul> <li>Images captured by the cameras;</li> <li>Cloud points captured by the LiDARs;</li> <li>Ground truth regarding the position of objects involved in the scene (vehicles, pedestrians).</li> </ul>