@conference {DasEtAl2002,
	title = {Neural Net Model for Featured Word Extraction},
	booktitle = {InterJournal of Complex Systems},
	number = {539},
	year = {2002},
	abstract = {Search engines perform the task of retrieving information related to the user-supplied query words. This task has two parts; one is finding {\textquoteright}featured words{\textquoteright} which describe an article best and the other is finding a match among these words to user-defined search terms. There are two main independent approaches to achieve this task. The first one, using the concepts of semantics, has been implemented partially. For more details see another paper of Marko et al., 2002. The second approach is reported in this paper. It is a theoretical model based on using Neural Network (NN). Instead of using keywords or reading from the first few lines from papers/articles, the present model gives emphasis on extracting {\textquoteright}featured words{\textquoteright} from an article. Obviously we propose to exclude prepositions, articles and so on, that is , English words like "of, the, are, so, therefore, " etc. from such a list. A neural model is taken with its nodes pre-assigned energies. Whenever a match is found with featured words and user-defined search words, the node is fired and jumps to a higher energy. This firing continues until the model attains a steady energy level and total energy is now calculated. Clearly, higher match will generate higher energy; so on the basis of total energy, a ranking is done to the article indicating degree of relevance to the user{\textquoteright}s interest. Another important feature of the proposed model is incorporating a semantic module to refine the search words; like finding association among search words, etc. In this manner, information retrieval can be improved markedly.},
	url = {http://uk.arxiv.org/abs/cs.NE/0206001},
	author = {Atin Das and M. Marko and A. Probst and M. A. Porter and C. Gershenson}
}
@article {MarkoEtAl2002,
	title = {Transforming the World Wide Web Into a Complexity-Based Semantic Network},
	journal = {InterJournal of Complex Systems},
	number = {588},
	year = {2002},
	url = {http://uk.arxiv.org/abs/cs.NI/0205080},
	author = {Matus Marko and M. A. Porter and A. Probst and C. Gershenson and A. Das}
}
@conference {GonzalezEtAl2001,
	title = {Integration of Computational Techniques for the Modelling of Signal Transduction},
	booktitle = {Advances in Systems Science: Measurement, Circuits and Control},
	year = {2001},
	publisher = {WSES Press},
	organization = {WSES Press},
	abstract = {A cell can be seen as an adaptive autonomous agent or as a society of adaptive autonomous agents, where each can exhibit a particular behaviour depending on its cognitive capabilities. We present an intracellular signalling model obtained by integrating several computational techniques into an agent-based paradigm. Cellulat, the model, takes into account two essential aspects of the intracellular signalling networks: cognitive capacities and a spatial organization. Exemplifying the functionality of the system by modelling the EGFR signalling pathway, we discuss the methodology as well as the purposes of an intracellular signalling virtual laboratory, presently under development.},
	url = {http://uk.arxiv.org/abs/cs.MA/0211030},
	author = {P. P. Gonz{\'a}lez and M. C{\'a}rdenas and C. Gershenson and J. Lagunez},
	editor = {N.E. Mastorakis and L.A. Pecorelli-Peres}
}
@conference {GershensonEtAl2000a,
	title = {Action Selection Properties in a Software Simulated Agent},
	booktitle = {{MICAI} 2000: Advances in Artificial Intelligence},
	series = {Lecture Notes in Artificial Intelligence},
	volume = {1793},
	year = {2000},
	pages = {634{\textendash}648},
	publisher = {Springer, Verlag},
	organization = {Springer, Verlag},
	address = {Acapulco, M{\'e}xico},
	abstract = {This article analyses the properties of the Internal Behaviour network, an action selection mechanism previously proposed by the authors, with the aid of a simulation developed for such ends. A brief review of the Internal Behaviour network is followed by the explanation of the implementation of the simulation. Then, experiments are presented and discussed analysing the properties of the action selection in the proposed model.},
	url = {http://uk.arxiv.org/abs/cs.AI/0211039},
	author = {C. Gershenson and P. P. Gonz{\'a}lez and J. Negrete},
	editor = {{O. Cair{\'o} and L. E. S{\'u}car, F.J. Cant{\'u}}
}
@conference {GershensonGonzalez2000,
	title = {Dynamic Adjustment of the Motivation Degree in an Action Selection Mechanism},
	booktitle = {Proceedings of {ISA} {\textquoteright}2000},
	year = {2000},
	address = {Wollongong, Australia.},
	abstract = {This paper presents a model for dynamic adjustment of the motivation degree, using a reinforcement learning approach, in an action selection mechanism previously developed by the authors. The learning takes place in the modification of a parameter of the model of combination of internal and external stimuli. Experiments that show the claimed properties are presented, using a VR simulation developed for such purposes. The importance of adaptation by learning in action selection is also discussed.},
	url = {http://uk.arxiv.org/abs/cs.AI/0211038},
	author = {C. Gershenson and P. P. Gonz{\'a}lez}
}
@conference {GonzalezEtAl2000b,
	title = {Modelling Intracellular Signalling Networks Using Behaviour-Based Systems and the Blackboard Architecture},
	booktitle = {Proceedings of the International Conference: Mathematics and Computers in Biology and Chemistry {(MCBC} 2000)},
	year = {2000},
	address = {Montego Bay, Jamaica},
	abstract = {This paper proposes to model the intracellular signalling networks using a fusion of behaviour-based systems and the blackboard architecture. In virtue of this fusion, the model developed by us, which has been named Cellulat, allows to take account two essential aspects of the intracellular signalling networks: (1) the cognitive capabilities of certain types of networks{\textquoteright} components and (2) the high level of spatial organization of these networks. A simple example of modelling of Ca2+ signalling pathways using Cellulat is presented here. An intracellular signalling virtual laboratory is being developed from Cellulat.},
	url = {http://uk.arxiv.org/abs/cs.MA/0211029},
	author = {P. P. Gonz{\'a}lez and C. Gershenson and M. C{\'a}rdenas and J. Lagunez}
}
@conference {GershensonEtAl2000b,
	title = {Thinking Adaptive: Towards a Behaviours Virtual Laboratory},
	booktitle = {{SAB} 2000 Proceedings Supplement},
	year = {2000},
	publisher = {ISAB press},
	organization = {ISAB press},
	address = {Paris, France},
	abstract = {In this paper we name some of the advantages of virtual laboratories; and propose that a Behaviours Virtual Laboratory should be useful for both biologists and AI researchers, offering a new perspective for understanding adaptive behaviour. We present our development of a Behaviours Virtual Laboratory, which at this stage is focused in action selection, and show some experiments to illustrate the properties of our proposal, which can be accessed via Internet.},
	url = {http://uk.arxiv.org/abs/cs/0211028},
	author = {C. Gershenson and P. P. Gonz{\'a}lez and J. Negrete},
	editor = {Jean-Arcady Meyer and Alain Berthoz and Dario Floreano and Herbert L. Roitblat and Stewart W. Wilson}
}
