- •Intelligent Agents
- •1. Introduction
- •2. The eva approach
- •3. Eva Architecture Overview
- •3.1 Nano-Agent Architecture
- •3.2 The Nanocheme Language
- •3.3 Artificial Life Primitives
- •3.4 Natural Language Interaction
- •4. The Experimental Prototype
- •4.1 Believable Intelligent Agents
- •4.2 Schizophrenic mental Model
- •4.3 Emotions
- •4.4 Memory and Web Mining
- •4.5 Graphical Interface
- •5. Results
- •5.1 Eva vs. Alice
- •5.2 Discussion
- •6. Conclusion and Future Works
- •Virtual Worlds
- •1. Introduction
- •2. Related work
- •2.1 Norms in web based communities
- •2.2 Combining multiagent systems and virtual environments
- •3. Modelling activities in a social virtual world
- •3.1 Our approach: a hybrid system with software agents and humans in 3d virtual
- •3.2 An organization based Multiagent System
- •3.3 Intelligent objects to control and assist participants' activities
- •4. Generic framework to enforce norms in svw
- •4.1 General description
- •4.2 Prototype
- •5. Conclusions
- •1. Introduction
- •2. Background & Related Work
- •2.1 Background
- •2.2 Related Work
- •2.2.1 Web portal system
- •2.2.2 Service oriented architecture
- •2.2.3 Single sign-on approach
- •2.2.4 Web resources monitoring
- •2.2.5 Site map
- •3. Design Objectives & Requirements
- •4. System description & implementation
- •4.1 Overall architecture
- •4.2 Redirect scheme & Single sign on service
- •4.2.1 Redirect scheme
- •4.2.2 Single sign on service
- •4.2.2.1 Single sign on components
- •4.2.2.2 Single sign on approaches
- •4.3 Portal design & implementation
- •4.3.1 Classification of function linkages
- •4.3.2 Access control capability
- •4.3.3 Ddnm design & implementation
- •4.3.4 Content of configuration file
- •4.3.5 A case study
- •4.3.6 DdnmLog Scheme
- •4.3.7 Additional tools
- •5. Achievements & performance evaluation
- •5.1 Achievements
- •6. Discussion
- •7. Conclusion
- •1. Introduction
- •2. The a3 approach to brain informatics
- •2.1 The a3 vision on the mechanism of mind
- •2.2 Place of a3 approach among other approaches
2.2 Place of a3 approach among other approaches
In order to correctly place the A3 approach among others, I will mention the features of
three main existing approaches and explain why they are incomplete for knowledge
representation purposes and for describing the mind content.
The relational model (Codd, 1970) was placed in the basis of database technology and the
entity-relationship approach (Chen, 1976) is successfully used in object management and
UML. Some authors regard a ‚world’ as represented by a database where all data about the
entities of such a world are persisted. Both these two main modeling frameworks, widely
used today in IT and AI implementations, don’t offer the level of precision required by
mathematics. Really, set theory is regarded as the main formalization framework for
mathematics and other disciplines, but the notion of set and ordered pair (treated as „element
of order“ ), not only are not among the primitive notions of these approaches, but are used
uncounsciously. Set theorists know that such attitude to the intuitive notion of set can raise
serious logical contradictions. In software, logical contradictions manifest as bugs.
Therefore, the software for representation of mind content developed according approaches
which unconsciously treat the basic operations of mind will, probably, have „conceptual
bugs“ in their specification and will not work.
The sound mathematical foundation of Semantic Web is reflected in its standards by
formulation of semantics of standards in the language of set theory. But Semantic Web is
focused on the discourse about the Universe, rather than the representation of the Universe.
The representation capabilities are an essential feature of an intelligent agent, and a
representation framework missing in Semantic Web might be responsible for the fact that, so
far, there is no proposal of a generic agent for processing the Semantic Web data.