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An introduction to RoboCup and Soccer Simulation 2D
An introduction to RoboCup and Soccer Simulation 2D

... It provides a standard problem for the evaluation of various theories, algorithms and architectures. It abstracts from hardware issues and focuses on subjects such as skill learning, coordination techniques, and opponent modeling. It is much easier (and cheaper) to test a simulation team against dif ...
Overview - Computer Science Department
Overview - Computer Science Department

... • Omniscience: the outcome of its actions are known. Impossible! • Learning: taking actions in order to perform better (e.g. robot vacuum cleaner) • Autonomy: the agent relies on its own sensors rather than built-in knowledge ...
A DAI Perspective on Cooperating Knowledge
A DAI Perspective on Cooperating Knowledge

... Distributed artificial intelligence is a melding of artificial intelligence with distributed computing. From artificial intelligence comes the theory and technology for constructing or analyzing an intelligent system. But where artificial intelligence uses psychology as a source of ideas, inspirati ...
Agent definitions - Computer Science
Agent definitions - Computer Science

... • Not to enter a debate about what intelligence is • Agent = more defined by its characteristics many of them may be considered as a manifestation of some aspect of intelligent behaviour. Adina Florea, 2001 ...
What Is an Intelligent Agent?
What Is an Intelligent Agent?

... “The American Heritage Dictionary defines an agent as “one that acts or has the power or authority to act… or represent another” or the “means by which something is done or caused; instrument.” The term derives from the present participle of the Latin verb agere: to drive, lead, act, or do.” 1 “The ...
SCHEME OF INSTRUCTION & EXAMINATION
SCHEME OF INSTRUCTION & EXAMINATION

Language Emergence and Grounding in Sensorimotor Agents and
Language Emergence and Grounding in Sensorimotor Agents and

Distributed Artificial Intelligence
Distributed Artificial Intelligence

... fall under this category. Agents also need the ability to reason about their own actions and the needs of other agents. Hence, development of reasoning mechanisms, either logic based or utility based, are important from individual perspective. Finally, the agents need to adapt to changing situations ...
A bayesian computer vision system for modeling human interactions
A bayesian computer vision system for modeling human interactions

... Artificial Intelligence perspective, behavior models for interacting agents are needed to interpret the set of perceived actions and detect eventual anomalous behaviors or potentially dangerous situations. Moreover, all the processing modules need to be integrated in a consistent way. Our approach t ...
From AUDREY to Siri. - International Computer Science Institute
From AUDREY to Siri. - International Computer Science Institute

Shah_Malalur - Computer Science
Shah_Malalur - Computer Science

...  But they work in complex and dynamic environment between the user and system resources.  Pre-determines data resources.  Example: Oracle’s ConText. ...
Concepts of Object- and Agent-Oriented Simulation
Concepts of Object- and Agent-Oriented Simulation

... entities of the model's domain as agents. They provide the environment and mechanisms to conduct controlled experimentation with software components developed in single or distributed Artificial Intelligence [7, 8], serve as a simulation environment for autonomous senso-motoric systems [9], or inten ...
Formalisms for Multi-Agent Systems
Formalisms for Multi-Agent Systems

... In summary, there is general agreement about the role that formalisms should play in multi-agent systems, but a very healthy diversity of approaches to fulfilling that role. As with mainstream computer science, our aim is to build effective and efficient computer systems, and theory is often justifi ...
behavioral animation for crowd simulation
behavioral animation for crowd simulation

Exploring coordination properties within populations of distributed agents Elizabeth Sklar
Exploring coordination properties within populations of distributed agents Elizabeth Sklar

... current plan to their neighbors, results are improved over situations where there is no communication at all. Auctions, in economic terms, are market mechanisms – for selling and/or buying some commodity – in which messages that agents send are indications of how much they are willing to pay (or acc ...
From: AAAI Technical Report S-9 -0 . Compilation copyright © 199
From: AAAI Technical Report S-9 -0 . Compilation copyright © 199

Argumentation for Resolving Privacy Disputes in Online Social
Argumentation for Resolving Privacy Disputes in Online Social

... :bob proves the contrary of AA1 . :bob updates the message and sends it to :alice. When :alice receives the message again, it checks whether it can attack AB2 . Since it has no supporting knowledge in its ontology, it consults :carol (a friend of :alice) to gather extra information for attacking AB2 ...
AAAI 2001 Spring Symposium Series Reports
AAAI 2001 Spring Symposium Series Reports

... interactions in systems composed of many such agents. Decision theory has been adopted as a paradigm for designing agents that can handle the uncertainty of any moderately complex environments and act rationally to achieve their goals (or preferences). Decision theory defines rationality as behavior ...
Computational Social Science
Computational Social Science

this PDF file - Trends Economics and Management
this PDF file - Trends Economics and Management

... The extent of the effects of technological development has aroused growing interest. The HITECH process has been studied by many disciplines having to do with a broad spectrum of socioeconomic phenomena. Over recent years, advances have been made in bringing together contributions proceeding from di ...
The Sea Battle Tomorrow: The Identity of Reflexive Economic Agents
The Sea Battle Tomorrow: The Identity of Reflexive Economic Agents

... For Merton, a social structure of interaction (the examiner and the depositors) is affected by the actions of an agent (the examiner’s evaluation) that produce further agent actions (depositors’ withdrawals), this acts on social structure (the relation between examiners and depositors), and possibly ...
What is Artificial Intelligence?
What is Artificial Intelligence?

... How do we represent or abstract or model the world? • Single agent (vs. multi-agent): An agent operating by itself in an environment. Does the other agent interfere with my performance measure? ...
Towards a New Approach in Social Simulations
Towards a New Approach in Social Simulations

Hafiz Noordin Term Paper - Engineering Computing Facility
Hafiz Noordin Term Paper - Engineering Computing Facility

... progress has been made in simulating dynamics of the systems [2]. In modeling the visual cortex, this is certainly an issue, as the difficulties lie more in the interactions of network components. The basic “Framework of Systems Biology” [1] consists of: ...
APPLIED COMPUTATIONAL INTELLIGENCE FOR FINANCE AND
APPLIED COMPUTATIONAL INTELLIGENCE FOR FINANCE AND

... to predict. Given that, this challenge is usually beyond the limits of mathematical analysis, simulation plays a key role in tackling this kind of problem. Those new to the field willing to gain some insight into this area might consider several research works, such as LeBaron (2000), Tesfatsion (20 ...
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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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