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On-line Error Analysis Using AI techniques A first sight
On-line Error Analysis Using AI techniques A first sight

... • After giving an answer, can justify or explain it by showing which knowledge they used to come to that conclusion. • Ιn a similar way can explain why they have asked their user a particular question. • Can imitate the performance of a human expert in a narrow domain of expertise, such as mortgage ...
Мултидисциплинарен подход за симулиране на 3D средата при
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... The application of traditional hydrogeological means and vehicles to the deterministic approach most often brings to difficulties. The attempts to determine 3-D objects based on statistical processing of results from “in situ” and laboratory filtration (hydraulic) tests (Боревский и др., 1973, Мирон ...
A MULTI-AGENT SYSTEM WITH APPLICATION IN PROJECT
A MULTI-AGENT SYSTEM WITH APPLICATION IN PROJECT

... term memory for all agents becomes a necessity to be added as long as project planning and scheduling process as repetitive activity for a project manager and therefore dependent to human experience and knowledge. For perceptions, we use a short term memory called Perception Buffer, which can be acc ...
From Agent Theory to Agent Construction: A Case Study
From Agent Theory to Agent Construction: A Case Study

... an implemented system and then formalizing the semantics in an agent language which can be viewed as an abstraction of the implemented system, and which allows agent programs to be written and interpreted [26]. Goodwin has also attempted to bridge the gap by providing a formal description in the for ...
Pogamut 3 Can Assist Developers in Building AI (Not Only) for Their
Pogamut 3 Can Assist Developers in Building AI (Not Only) for Their

... package. Moreover, they are not included into the Pogamut 3 release version at the time of writing of this paper, but they may be available for download in coming months. In this section, we will review these extensions in further detail: ACT-R Integration, Gestures module, ALMA Integration, and Edu ...
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What is AI?
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Influence-based Abstraction for Multiagent Systems
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... POSGs can be decomposed into local models. We formalize the interface between such local models as the influence agents can exert on one another; and we prove that this interface is sufficient for decoupling them. The resulting influence-based abstraction substantially generalizes previous work on e ...
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System Intelligence, Knowledge Systems and Darwin
System Intelligence, Knowledge Systems and Darwin

From Agent Theory to Agent Construction: A Case Study
From Agent Theory to Agent Construction: A Case Study

... an implemented system and then formalizing the semantics in an agent language which can be viewed as an abstraction of the implemented system, and which allows agent programs to be written and interpreted [26]. Goodwin has also attempted to bridge the gap by providing a formal description in the for ...
From Agent Theory to Agent Construction: A Case Study
From Agent Theory to Agent Construction: A Case Study

... an implemented system and then formalizing the semantics in an agent language which can be viewed as an abstraction of the implemented system, and which allows agent programs to be written and interpreted [26]. Goodwin has also attempted to bridge the gap by providing a formal description in the for ...
AI Research in the 21st Century
AI Research in the 21st Century

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... the optimal sequence of actions always puts the block on the same x-position. Since the xdimension is the only dimension used to represent state, the agents update the same Q-value in their policy matrix in successive steps. We now calculate the number of updates required for the Q-value correspondi ...
here - FER
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... mechanisms has been empirically shown to lead to substantial performance improvements in several typical evolutionary robotics experiments [3]. Several novel frameworks for studying cognitive systems have arisen from devising and implementing distributed approaches to the problem. Swarm cognition is ...
Computational Intelligence Approaches for Student/Tutor
Computational Intelligence Approaches for Student/Tutor

... Knowledge representation and management is a vital issue in both student/expert model designs and it is equally not surprising to see how the use of ontology is recently gaining popularity. The data mining techniques too appear to be pairing well recently, the use of such techniques like machine lea ...
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CS 363 Comparative Programming Languages

... – How better to spend five weeks of summer than learning Computer Science with 140 of your closest TJ friends. A great way to fit more into your cramped schedule, the summer school program thrives because of students aides (upperclassmen, apply early). An accelerated class is usually formed during t ...
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Agent-based model

An agent-based model (ABM) is one of a class of computational models for simulating the actions and interactions of autonomous agents (both individual or collective entities such as organizations or groups) with a view to assessing their effects on the system as a whole. It combines elements of game theory, complex systems, emergence, computational sociology, multi-agent systems, and evolutionary programming. Monte Carlo Methods are used to introduce randomness. Particularly within ecology, ABMs are also called individual-based models (IBMs), and individuals within IBMs may be simpler than fully autonomous agents within ABMs. A review of recent literature on individual-based models, agent-based models, and multiagent systems shows that ABMs are used on non-computing related scientific domains including biology, ecology and social science. Agent-based modeling is related to, but distinct from, the concept of multi-agent systems or multi-agent simulation in that the goal of ABM is to search for explanatory insight into the collective behavior of agents obeying simple rules, typically in natural systems, rather than in designing agents or solving specific practical or engineering problems.Agent-based models are a kind of microscale model that simulate the simultaneous operations and interactions of multiple agents in an attempt to re-create and predict the appearance of complex phenomena. The process is one of emergence from the lower (micro) level of systems to a higher (macro) level. As such, a key notion is that simple behavioral rules generate complex behavior. This principle, known as K.I.S.S. (""Keep it simple, stupid"") is extensively adopted in the modeling community. Another central tenet is that the whole is greater than the sum of the parts. Individual agents are typically characterized as boundedly rational, presumed to be acting in what they perceive as their own interests, such as reproduction, economic benefit, or social status, using heuristics or simple decision-making rules. ABM agents may experience ""learning"", adaptation, and reproduction.Most agent-based models are composed of: (1) numerous agents specified at various scales (typically referred to as agent-granularity); (2) decision-making heuristics; (3) learning rules or adaptive processes; (4) an interaction topology; and (5) a non-agent environment. ABMs are typically implemented as computer simulations, either as custom software, or via ABM toolkits, and this software can be then used to test how changes in individual behaviors will affect the system's emerging overall behavior.
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