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--- geopro:pedro:platforms [2007/07/17 01:20] – pedro
+++ geopro:pedro:platforms [2008/06/12 18:35] (atual) – pedro
@@ Linha 1: / Linha 1: @@
-====== MAS infrastructures======
+====== ABM Discussions and Requirements======
+====Anatomy of a Toolkit: A comprehensive compendium of various agent-based modelling toolkits, on the market today====
+|C. Nikolay, G. Madey, 2007| Proceedings of Agent2007: Complex interaction and social emergence, 87-97| [[http://leg.ufpr.br/~pedro/papers/anatomy-toolkit.pdf|pdf]]|
+\\
+**Abstract:** With so many toolkits available, the choice of which one is best suited for your project
+can be overwhelming. Moreover, different communities of users prefer different aspects
+of a toolkit. This paper is a survey of the toolkits that are available today and how they
+compare to each other from a multi-stakeholder perspective. Our goal is to provide users
+the ability to better choose a suitable toolkit based on the features abstracted from various
+documentation and the first hand experiences of a broad range of communities of users
+and compiled into an easy to use compendium. In addition, we expand the Agent Based
+Modeling body of knowledge to include information about a breadth of characteristically
+and historically diverse platforms.
+\\
 ====Evaluation of free Java-libraries for social-scientific agent based simulation====
@@ Linha 54: / Linha 72: @@
 At the end of the paper, there is a long list of other tools, and the reasons why they were excluded from the analysis.
 ====Requirements Analysis of Agent-Based Simulation Platforms: State of the Art and New Prospects====
-|M. B. Marietto, N. David, J. S. Sichman, H. Coelho, 2003|[[http://www.springerlink.com/content/c22cux8vq0uydkyy/|LNCS]]|[[http://scholar.google.com.br/scholar?hl=pt-BR&lr=&cites=9291166468364790291|9 citations in Scholar]]|
+|M. B. Marietto, N. David, [[http://www.pcs.usp.br/~jaime/#projetos|J. S. Sichman]], H. Coelho, 2003|[[http://www.springerlink.com/content/c22cux8vq0uydkyy/|LNCS]]|[[http://scholar.google.com.br/scholar?hl=pt-BR&lr=&cites=9291166468364790291|9 citations in Scholar]]|
 |M. B. Marietto, N. David, J. S. Sichman, H. Coelho, 2002|Multi-Agent Based Simulation Workshop| [[http://leg.ufpr.br/~pedro/papers/requirement-analysis-MABS.pdf|pdf]]| [[http://scholar.google.com.br/scholar?hl=pt-BR&lr=&cites=4620433597513998219|4 citations in Scholar]]|
@@ Linha 214: / Linha 233: @@
 unexpected outcomes are a reflection of a mistake in the computer programme (a ‘bug’),
 logical errors of the model, or a surprising consequence of the model itself (Gilbert and Terna, 1999).
 ====Agent-based Simulation Platforms: Review and Development Recommendations====
@@ Linha 238: / Linha 258: @@
 Our focus is primarily on the "ease of use" issue: how easy is to implement ABMs and conduct experiments on them?
-There is a table comparing the terminology in five platforms. They have implemented some versions of a //stupid model// in all five tools.
+There is a table comparing the terminology in five platforms. They have implemented some versions of a //stupid model// in [[http://www.swarm.org/wiki/Software_templates|all five tools]].
 ^ Version  ^ Characteristics Added                                                                                   ^ TerraME|
@@ Linha 291: / Linha 311: @@
+====Platforms and methods for agent-based modeling====
+|N. Gilbert and S. Bankes, 2002|National Acad Sciences|[[http://leg.ufpr.br/~pedro/papers/platforms_and_methods.pdf|pdf]]|[[http://scholar.google.com.br/scholar?hl=pt-BR&lr=&cites=6771659880706703459|31 citations in  Scholar]]|
+\\
-=====TODO=====
+**Abstract:** The range of tools designed to help build agent-based models is brieﬂy reviewed. It is suggested that although progress has been made,
+there is much further design and development work to be done. Modelers have an important part to play, because the creation of tools
+and models using those tools proceed in a dialectical relationship.
-====Computational Laboratories for Spatial Agent-Based Models====
-|C. Dibble, 2006| [[http://ideas.repec.org/h/eee/hecchp/2-31.html| Handbook of Computational Economics ]]|
 \\
-**Abstract:** An agent-based model is a virtual world comprising distributed heterogeneous agents who interact over time. In a spatial agent-based model the agents are situated in a spatial environment and are typically assumed to be able to move in various ways across this environment. Some kinds of social or organizational systems may also be modeled as spatial environments, where agents move from one group or department to another and where communications or mobility among groups may be structured according to implicit or explicit channels or transactions costs. This chapter focuses on the potential usefulness of computational laboratories for spatial agent-based modeling. Speaking broadly, a computational laboratory is any computational framework permitting the exploration of the behaviors of complex systems through systematic and replicable simulation experiments. A narrower definition, used here, refers more specifically to specialized software tools to support a wide range of tasks associated with agent-based modeling. These tasks include model development, model evaluation through controlled experimentation, and both the descriptive and normative analysis of model outcomes. This chapter examines how computational laboratory tools and activities facilitate the systematic exploration of spatial agent-based models embodying complex social processes critical for social welfare. Examples include the spatial and temporal coordination of human activities, the diffusion of new ideas or of infectious diseases, and the emergence and ecological dynamics of innovative ideas or of deadly new diseases.
+The authors compare the standardization that occurred in statistical packages to the development of ABM,
+and the advantages over "rolling your own", and the limitations of having to know the programming language. They talk a bit
+about the following tools: repast, swarm, ascape, starlogo, agentsheets, sdml, cormas, desire. //The facilities for other phases of a model’s
+life cycle, model evaluation, model maintenance, and many types of model use are rather limited at this time. The primary
+supports for model use are visualizations of model state (especially the ubiquitous displays of two-dimensional grids of agent
+positions) and modest facilities for collecting statistics in a single run.//
+Issues: comparing multiple model runs, loading or calibrating models from data, automatically generating large numbers of cases
+from experimental designs, collecting and statistically analyzing the results of large numbers of experiments.
-\\
@@ Linha 312: / Linha 343: @@
 \\
+One element that we articulate is the relation between infrastructure for a single agent and the infrastructure for the MAS
+in which the agent participates. We consider MAS infrastructure to be the domain independent and reusable substratum on which MAS systems, services, components, live,
+communicate, interact and interoperate, while the single agent infrastructure is the generic parts of an agent that enable it to be part of a multiagent society.
-====Environments for Multiagent Systems, State-of-the-Art and Research Challenges====
+[The infrastructure is clearly for modelling agents in different machines, but we can use the same concepts for simulating.]
-|D. Weyns, H. V. D. Parunak, F. Michel, T. Holvoet and J. Ferber, 2005| [[http://www.springerlink.com/content/lhc33yytty1el5mf/|Environments for Multi-Agent Systems (LNCS)]]|[[http://scholar.google.com.br/scholar?hl=pt-BR&lr=&cites=9006150710654133145|53 citations in Scholar]]|
+Some of the layers presented are (the complete list is [[http://leg.ufpr.br/~pedro/figures/retsina-infrastructure.jpg|here]]):
+  - ACL (Agents Communication Language): it enables agents to be implemented in almost any language
+  - Mapping names to agent locations
+  - Performance measurement
+  - Locating agents by capability
-(some interesting papers cite this one): "Agents are not part of the problem, agents can solve the problem", "Environments in multiagent systems", "Environment as a first class abstraction in multiagent systems", "Environments for Situated Multi-Agent Systems: Beyond Infrastructure"
+When an agent first comes up in an open environment, it may want to register itself with agent name services.
+Instead of having hardwired IP addresses for such services, the MAS infrastructure
+and the corresponding single agent infrastructure can facilitate the discovery of existing registered agents.
-**Abstract:** It is generally accepted that the environment is an essential compound of multiagent systems (MASs). Yet the environment is typically assigned limited responsibilities, or even neglected entirely, overlooking a rich potential for the paradigm of MASs.
+__TerraME:__ Instead of having the possibility of finding agents according to the capability, the agents can be located according
-Opportunities that environments offer, have mostly been researched in the domain of situated MASs. However, the complex principles behind the concepts and responsibilities of the environment and the interplay between agents and environment are not yet fully clarified.
+to a tag, that can store the "class" of the agent. Or perhaps the agent can registry itself using another argument representing
-In this paper, we first give an overview of the state-of-the-art on environments in MASs. The survey discusses relevant research tracks on environments that have been explored so far. Each track is illustrated with a number of representative contributions by the research community. Based on this study and the results of our own research, we identify a set of core concerns for environments that can be divided in two classes: concerns related to the structure of the environment, and concerns related to the activity in the environment. To conclude, we list a number of research challenges that, in our opinion, are important for further research on environments for MAS.
+this tag.
-\\
+This information is called the agent’s capability advertisement and is provided by the agent to a middle agent.
+When an agent needs another that has some required capability, it sends a middle agent a
+request specifying the desired capability. The middle agent matches requests and
+advertisements. In general, there could be a variety of middle agents that exhibit different
+matching behaviors. we have identified 28 middle agent types and have experimented with different performance
+characteristics.
-====Platforms and methods for agent-based modeling====
+__Discussion:__ How to locate an middle agent?
-|N. Gilbert and S. Bankes, 2002|National Acad Sciences|[[http://leg.ufpr.br/~pedro/papers/platforms_and_methods.pdf|pdf]]|[[http://scholar.google.com.br/scholar?hl=pt-BR&lr=&cites=6771659880706703459|31 citations in  Scholar]]|
+**Open systems** allow agents to enter, and exit, the system dynamically and unpredictably, while **closed systems**
+employ a  fixed set of agents that are known a priori. In closed MAS each agent knows the name, location and capability
+of the others. Thus agent interactions can be statically predefined. This makes agent design and construction
+simple, but makes the MAS brittle and not  extensible.
+====Modelling social action for AI agents====
+|C. Castelfranchi, 1998| Applied Artificial Intelligence|
+=====TODO=====
+====Computational Laboratories for Spatial Agent-Based Models====
+|C. Dibble, 2006| [[http://ideas.repec.org/h/eee/hecchp/2-31.html| Handbook of Computational Economics ]]|
 \\
-**Abstract:** The range of tools designed to help build agent-based models is brieﬂy reviewed. It is suggested that although progress has been made,
+**Abstract:** An agent-based model is a virtual world comprising distributed heterogeneous agents who interact over time. In a spatial agent-based model the agents are situated in a spatial environment and are typically assumed to be able to move in various ways across this environment. Some kinds of social or organizational systems may also be modeled as spatial environments, where agents move from one group or department to another and where communications or mobility among groups may be structured according to implicit or explicit channels or transactions costs. This chapter focuses on the potential usefulness of computational laboratories for spatial agent-based modeling. Speaking broadly, a computational laboratory is any computational framework permitting the exploration of the behaviors of complex systems through systematic and replicable simulation experiments. A narrower definition, used here, refers more specifically to specialized software tools to support a wide range of tasks associated with agent-based modeling. These tasks include model development, model evaluation through controlled experimentation, and both the descriptive and normative analysis of model outcomes. This chapter examines how computational laboratory tools and activities facilitate the systematic exploration of spatial agent-based models embodying complex social processes critical for social welfare. Examples include the spatial and temporal coordination of human activities, the diffusion of new ideas or of infectious diseases, and the emergence and ecological dynamics of innovative ideas or of deadly new diseases.
-there is much further design and development work to be done. Modelers have an important part to play, because the creation of tools
-and models using those tools proceed in a dialectical relationship.
 \\
-The authors compare the standardization that occurred in statistical packages to the development of ABM,
-and the advantages over "rolling your own", and the limitations of having to know the programming language. They talk a bit
-about the following tools: repast, swarm, ascape, starlogo, agentsheets, sdml, cormas, desire. //The facilities for other phases of a model’s
-life cycle, model evaluation, model maintenance, and many types of model use are rather limited at this time. The primary
-supports for model use are visualizations of model state (especially the ubiquitous displays of two-dimensional grids of agent
-positions) and modest facilities for collecting statistics in a single run.//
-Issues: comparing multiple model runs, loading or calibrating models from data, automatically generating large numbers of cases
-from experimental designs, collecting and statistically analyzing the results of large numbers of experiments.
+====Environments for Multiagent Systems, State-of-the-Art and Research Challenges====
+|D. Weyns, H. V. D. Parunak, F. Michel, T. Holvoet and J. Ferber, 2005| [[http://www.springerlink.com/content/lhc33yytty1el5mf/|Environments for Multi-Agent Systems (LNCS)]]|[[http://scholar.google.com.br/scholar?hl=pt-BR&lr=&cites=9006150710654133145|53 citations in Scholar]]|
+(some interesting papers cite this one): "Agents are not part of the problem, agents can solve the problem", "Environments in multiagent systems", "Environment as a first class abstraction in multiagent systems", "Environments for Situated Multi-Agent Systems: Beyond Infrastructure"
+**Abstract:** It is generally accepted that the environment is an essential compound of multiagent systems (MASs). Yet the environment is typically assigned limited responsibilities, or even neglected entirely, overlooking a rich potential for the paradigm of MASs.
+Opportunities that environments offer, have mostly been researched in the domain of situated MASs. However, the complex principles behind the concepts and responsibilities of the environment and the interplay between agents and environment are not yet fully clarified.
+In this paper, we first give an overview of the state-of-the-art on environments in MASs. The survey discusses relevant research tracks on environments that have been explored so far. Each track is illustrated with a number of representative contributions by the research community. Based on this study and the results of our own research, we identify a set of core concerns for environments that can be divided in two classes: concerns related to the structure of the environment, and concerns related to the activity in the environment. To conclude, we list a number of research challenges that, in our opinion, are important for further research on environments for MAS.
+\\
@@ Linha 355: / Linha 416: @@
  managing dynamic heterogeneity
 ====Software engineering considerations for individual-based models====
 |Ropella, G. E. P., S. F. Railsback, and S. K. Jackson. 2002| Natural Resource Modeling|
+\\
+understanding causality?