INSEA, Rabat, Morocco
Intelligent Systems: A brief historical overview
Intelligent System has a polysemic meaning; it is used in different contexts to refeer to various types of information systems. To get an idea of what we mean by Intelligent System, we propose to consider the development of its meaning in Artificial Intelligence (AI); the Science of Intelligent Systems, and Cognitive Science; the Science of Cognitive System. Basically, we can identify four periods concerning the development of Intelligent Systems, each period provides and explores different types of intelligent systems.
Note: the following presentation is very informal and without references. Also, the number and the timing of periods are suggestive only. The aim is to provide a historical canvas for presenting briefly the rapid development of Intelligent Systems in AI and Cognitive Science.
1. Period 1 (1956 to 1970), Exploration of intelligent processes and development of the first intelligent systems: first exploration of various intelligent (mental) processes: problem solving, games, theorem proving, reasoning, natural language processing and learning. Many programs were developed in this period. The main conclusions were: a) intelligent processes are very complex, b) the need and importance of knowledge, c) the need and the importance of meta-knowledge (knowledge about knowledge), d) the importance of "search space" paradigm.
2. Period 2 (1970 to 1986), Intelligent Systems are Knowledge, Meta-Knowledge and Intentional-Based Systems : It became evident that knowledge should constitute the kernel of any intelligent system; intelligent systems are Knowledge-Based Systems. The dominant definition of AI at that time was that "AI is the science of Knowledge-Based Systems". Main topics emerged: knowledge structures, "meaning" of knowledge (what we call ontology actually), knowledge representations, operations on knowledge structures, meta-knowlege, context for knowledge and related issues such as background knowledge and common sense. There was also the necessity to consider various intelligent processes as knowledge-based processes. Semantic Networks and Conceptual Structures, such as Script, Schema, Frame, Prototype and Scenario were proposed and used to deal with the fuzzy and complex notion of context. Also, the complexity of background knowledge and common sense, and the complexity of conceptual structures and intelligent processes became more and more insistent. To accommodate this complexity, researchers subdivided intelligent processes to many subprocesses: for instance natural language processing was subdivided into morphology, lexicology, parsing (syntax), understanding (semantics and pragmatics), generation, interaction (dialog), etc. However, researchers were more and more aware that not only subprocesses but also processes are highly related and dependent. For instance, to develop a "real" natural language processing system, we need to consider all language levels (morphology, lexicology, syntax, semantic, pragmatic). Moreover, natural language processing is based on inferences, problem solving and planning. Behavior or planning is based also on inference/reasoning, on knowledge and on natural language processing (if the behavior involves interaction and communication in natural languages). Thus, the need for a systemic and integrated approach became insistent. Other knowledge related issues were also considered: truthfulness, certainty and modality of knowledge, relation of knowledge to belief, to time, to intention, to knowledge inference strategies, to knowledge acquisition, etc. Another important topic at this time was that intelligent systems are Intentional Systems, i.e. knowledge of the world and knowledge about actions, plans and goals is used by an intelligent system to achieve its intentions or goals (and also to react to the envionment, this can be viewed as a goal). Another important aspect that became more and more important is the modularity of knowledge: even if knowledge is highly connected, there should be some mechanisms to form chunks of knowledge that are used in specific contexts. This aspect was captured by the distinction made between the whole context (ultimately the memory) which is considered as a huge semantic network, and conceptual structures which are conceptual units or chunks of knowledge. Another important discovery of this period concerns the relation between structures and processes: in computer science (from its begining to the eighties), there was a clear distinction between data (structures) and procedures (actions, procedures, functions, processes or treatment/behavior in general). This distinction was "inherited" by AI, but in computer science as well as in AI, researchers were aware more and more that this sharp distinction between the two aspects of information (descriptive vs prescriptive information) is not mandatory. Researchers started to model dynamic objects; objects with both descriptive vs prescriptive information. In AI, Frame (proposed by Minsky in 1975) was the main representative of dynamic object. But another important formulation of dynamic object, proposed by Norman, Rumelhart and the LNR group in their important book "Explorations in Cognition, 1975", was their active semantic networks. These two formulations of dynamic objects followed two different paths: Frame was defined basically as a set of roles, each role has a filler. In the first versions of Frame, a filler corresponds to a data (simple or structured) with some attributes and procedures that can be activated by different ways. The structural part of such a Frame was the slots and the attributes of the fillers. The behavioral part was the procedures attached to the fillers. Later (in the eighties), it became clear that a filler can be itself a Frame. Hence, the simple structure of a Frame evolved to a network-like structure where fillers correspond to nodes of a network, slots to links/relations between fillers and the filler's attributes and procedures correspond to properties attached to fillers/nodes. Frame becomes in this way a kind of active semantic network !! Let us consider now the development of ASN of the LNR group. To understand this development, note that Norman (and most of the members of the LNR group) was a cognitive psychologist and a leader of the emerging cognitive science. He was interested in human cognition and the LNR group developed intelligent systems to modelize, understand and analyze human cognition. One main topic that became central in the emerging cognitive science in general and for the LNR group in particular turns around the following question: if human cognition is based on knowledge representation in general and on ASN in particular, how these networks are "represented" in the brain, if they are represented in the first place !? Smoothly, the group shifted from active semantic networks to (active) neural networks (neural networks are active by definition) !! From the implementation of ASN on computers, the group shifted to the (eventual) implementation on neural networks. And the problem in fact was not how to implement ASN with neural networks but how ASN could emerge from neural networks (in the sense that at the cognitive level, neural network activity can be "viewed as" an ASN). This shift of interest concerning the LNR group had its impact on AI: in the eighties, great attention and emphasis were made by AI researchers on neural networks and "distributed knowledge and knowledge without representation". The LNR group was "replaced" by the PDP group (Rumelhart worked with McClelland) and the book "Parallel Distributed Processing, 1986" marks the official beginning of "neural networks era". Rumelhart, with the PDP group, focused on "low cognition" while Norman followed his work on "high cognition". Active Semantic Networks, as proposed by LNR group, was not developed further. The period 1980-1985 was the foundation period of Cognitive Science (or Science of Cognition), a multi-disciplinary new science (cognitive psychology, AI, linguistics, philosophy) that focuses on cognition. Several books written by authors from the involved disciplines, were proposed as a foundation for this new science. One of these books was the book of John Sowa, "Conceptual Structures : Information Processing in Mind and Machines, 1984" In this book, Sowa proposed Conceptual Graph (CG) Theory as a foundation for "high cognition". CG theory is a synthesis from several fields: semantic networks and related topics in AI, logic (Sowa showed the equivalence of CG logic and predicate logic and he used Existential Graph Logic of S. C. Peirce as a logical foundation for CG theory), linguistics (as a semantic network notation, CG can be used to represent meaning and background knowledge), relational database semantics, cognitive psychology and philosophy (John showed how CG theory can represent various conceptual structures that were proposed and developed in cognitive psychology and philosophy). What is of interest here and in relation with Active Semantic Network (ASN), is that Sowa showed how CG (which is a general form of static semantic network) can be "merged" with a dataflow graph (developed in the field of parallel computation, dataflow graph is an important formulation and modelization of parallel computation). Starting with this proposition (the merge of CG and dataflow graph), A. Kabbaj proposed Active Conceptual Graph and developed the language SYNERGY which is based on this extended view of CG. Active CG can be viewed also as a development and a continuation of ASN. See SYNERGY for detail. At the philosophical and especially the epistemological level, the main questions during this period (1970-1986) were : what is knowledge, intelligence, meta-knowledge and intelligent systems ? we can summarize the result of the debate around these questions as follows: what is knowledge ?, for human, knowledge is the result of complex processes based on perception, abstraction, categorization, concept formation, experience, and learning. Guided by his knowledge and intention, humans are able to analyze income information from their perception senses and construct knowledge ("conceptual representations" ?) of the perceived world. In philosophy, a related topic to this conceptualization process is the problem of "reference" and "meaning"; the adequation of our mental representations (abstractions) to the reality, etc. Of course, the conceptualization process is a continuous process; it is related to other processes and previous knowledge becomes context for the interpretation of the new knowledge. Intelligent systems of this period did not have such a complex conceptualization process ; knowledge was given to the system by the programmer (due to this shortcut, many researchers claimed that those intelligent systems are not really intelligent, i.e. they are not intelligent like humans). Let us consider now the second question: what is intelligence? Of course, this is a difficult and complex question but the kernel for an answer may be expressed as follows: intelligence is the capability of a system to know what it knows and to know how to use its knowledge to achieve its goals. This "condensed response/formula" highlights the strong relation between intelligence and meta-knowledge (knowledge about knowledge). It refeers also to the capability of an intelligent system to use its knowledge in decision making and in achieving its goals. Heuristics, strategies, meta-rules, meta-goals, meta-plans and context are forms of meta-knowledge. Indeed, context (in terms of script, frame or any other complex structures) is a kind of meta-knowledge since it provides the relevant knowledge that enables the interpretation and the organization of the new knowledge. In sum, intelligence is based on knowledge, meta-knowledge and intention. Knowledge, Meta-Knowledge and Intentional Knowledge (goals, plans, actions, motivation, belief, etc.) were given to intelligent systems of this period by the programmer(s) as a set of programs. Thus, intelligence was not an emergent property of the system; it was an "external intelligence" (provided by the programmer as a set of programs). The challenge for the next period(s) was to develop systems with their own intelligence, i.e. systems that can develop their own knowledge from the perceived world (or environment), that can develop their own meta-knowledge and their own intentional knowledge.
3. Period 3 (1980 to 1996) Toward Autonomous Intelligent Systems: Several threads of research were active in this period : a) one thread continued to explore knowledge-based systems (to study knowledge structures, knowledge representation, knowledge meaning or ontology, context, knowledge-based processes, etc.); b) another thread started to explore the integrated approach concerning knowledge and processes; c) another thread was interested in the development of neural networks and connexionist models and machines to deal with the capability of the system to perceive its own environment and to get knowledge from its own perception. "Distributed knowledge and knowledge without representation" was an important feature of these models; d) another thread was concerned by the nature of meta-knowledge and how a system can develop its own meta-knowldge. Meta-Knowledge can be developed from experiences and learning: as the system attempts to use its knowlege to understand, explain, resolve problems and satisfy its goals, the system will learn from these situations/experiences that become themselves knowledge ! Of course, memory and learning are the key components in this process: learning is a multi-strategy process (various learning strategies: generalization, analogy, abduction, explanation based learning, etc.) which operates on the new experiences and integrate them in memory, involving comparison, modification and connection of the new experiences with the old ones as well as reorganization and update of the memory. Learned experiences give the system the context, the meta-knowledge required for an intelligent behavior; e) another thread of research in this period was concerned by the source and development of intentional knowledge: not only goals and plans, but motivation, belief, emotion and other cognitive categories are required to provide a precise account of autonomous intentional systems. It became clear that autonomous intentional systems are "situated systems": the "body" and the various dimensions of the environment (society) have strong impact on the intentional knowledge and capabilities of the system; f) another thread focused on the interaction and communication of multi autonomous systems; g) another thread of research stressed the "evidence" that autonomous system (like human) is first a reactive system, in addition to being intelligent, intentional, and interactive. A "really" autonomous intelligent system should be able to react to any "perturbation" from the environment. Also, h) another thread of research focused on "collective or emerging intelligence": a reactive system can not be intelligent by itself but a community of such reactive systems can exhibit intelligent behavior.
4. Period 4 (1990 - to the present) Integrated Autonomous Intelligent Agents: This period is characterized, among other things, by the development of Case-Based Reasoning, Multi-Strategy Learning, Ontology, Agents and Multi-Agent Systems, Hybrid Systems (Symbolic and Connexionist), etc. In this period, all the previous threads has continued to progress. Also, great efforts have been deployed to achieve integration of different combination of these threads. Ontology, Agents and Multi-Agents Systems constitute the appropriate context to explore these integrations. It becomes clear that AI tools and platforms should provide an integrated architecture to build Intelligent Systems and Intelligent Agents.
Above, we noted that Artificial Intelligence (AI) is a Science of Intelligent Systems. In summary, we can say that the main goal of AI is the use of computer science and information processing as a paradigm to model Intelligence in order to develop Intelligent Systems and Intelligent Agents.
Without going in deep discussions about what are Intelligent Systems and Intelligent Agents, and what is the difference between the two, let us say that Intelligent System is any system that incorporates and performs an intelligent process/task (like problem solving, reasoning, planning, learning, natural language processing, etc.).
Like the notion of Intelligent System, the notion of "Intelligent Agent" recieved several definitions, proposed in the litterature, starting with a specification of a typologie of agents that produce different types/categories of Agents. The most important distinction is between "Cognitive Agent" and "Reactive Agent". A cognitive agent is an agent with cognitive capabilities (mental states, knowledge, belief, ... and cognitive processes like reasoning, learning, problem solving, planning, natural language processing, etc.). Since cognitive agents are considered in general in the context of Multi-Agents Systems, communication becomes another important cognitive process. A reactive agent is an agent with perceptual and acting capabilities with a kind of Stimulus-Response behavior.
Integrated agent is an agent that integrates both cognitive and reactive capabilities. Unlike intelligent system, an "ideal" integrated agent is an agent that integrates most of the cognitive and reactive capabilities and processes, becoming closer to Human (considered as an "ideal integrated agent") than to "a simple intelligent system". In this context, it becomes mandatory to speak of the architecture of "intelligent agent". Figure 1 provides an example of such an architecture. In our proposed architecture, an agent has basically three components:
· The kernel of an intelligent agent, composed of a Dynamic Memory and their related processes (knowledge dynamic integration process, information retrieval, etc.), Inference & Reasoning strategies, and Learning strategies. The content of Dynamic Memory is very rich, complex and diverse. It includes the agent knowledge of the world (i.e. the agent's ontology), including knowledge of himself and of other agents, diverse knowledge bases about various topics and expertises, and his various experiences.
· Cognitive Processes that are based on the kernel; they include Behavior (Problem Solving, Natural Language Processing, Intentional Planning, and Reactive behavior) and Understanding and Explaining.
· Perceptual processes, Communication and Physical Action. An agent communicate and act upon the world/environment (and the other agents) through perceptual processes, communication and physical action.
Figure 1: Architecture of an Intelligent Agent
Amine is a Java Open Source Platform suited for the development of different types of intelligent systems/agents:
The main aim/goal of Amine is to provide an integrated architecture to build Intelligent Systems and Intelligent Agents
Amine can be of interest not only for Artificial Intelligence and Cognitive Science, but also for Semantic Web. Amine results from more than 20 years of extensive research and work done by Pr. Adil Kabbaj (1985 to the present) concerning several topics in Conceptual Graphs (CG) Theory and AI. See about for more detail on Amine's background and history.
Amine has a multi-Layer architecture. In the first versions of Amine (versions 1-4), we adopted a four-layers or levels architecture (Figure 2) :
Figure 2: First version of Amine Architecture
1. The Kernel level: (Multi-Lingua Ontology Layer) which offers a possibility to create, edit and ask an ontology defined in terms of Conceptual Structures -CS- (definition, canon, individual and situation). Several conceptual lexicons can be associated to such an ontology (i.e. lexicons for English, French, Spanish, Arabic, etc.) allowing multi-lingua conceptual ontology. Since an ontology can be shared by several agents with different languages, we decided to develop a multi-lingua platform, i.e. a multi-lingua ontology and multi-lingua structures. Thus, users with different languages can express knowledge in their languages and still share all knowledge expressed in other languages. This is done in Amine thanks to the separation between conceptual elements (concept type, concept referent, relation type) and their identifiers in a specific language. Multi-lingua is one key feature of Amine platform. Another key feature of Amine is that Amine ontology is not restricted to a specific description scheme: for the description of Conceptual Structures (the basic components of Amine ontology), the developer can use CG, KIF, a Frame-like notation, RDF, XML or any description scheme that is implemented in Java (and, in preference, that implements some interfaces of AminePlatform like AmineObject and Matching). The kernel level is implemented as a Java package called "kernel" that contains itself two packages: "lexicons" and "ontology". The kernel is a “stand-alone” level; the "kernel" package can be used without the other levels and even without the adoption of any description scheme (see Ontology and Samples for more detail on this possibility). Also, beside the APIs for the creation and use of an ontology with its associated lexicons, Amine offers a graphic interface, LexiconsOntology GUI, to browse, create, edit, and update an ontology.
2. (the programming context that determines how variable binding should be interpreted and resolved, i.e. how to get the value of a variable). (like Amine structures) In addition to APIs for the creation and manipulation of Amine structures (including ), Amine platform provides a graphic interface, CGNotations GUI, which is an MDI, multi-lingua and multi-notation (LF, CGIF and Graphic) CG editors and another graphic interface, CGOperations GUI, for the application of CG operations on specific CGs.
3. Programming (or Engines) level/layer: Three complementary engines are provided: a) a dynamic ontology engine that allows an incremental and automatic formation and update of an ontology, b) PROLOG+CG that is an object based and CG-based extension of Prolog language and, c) SYNERGY which is a CG-activation and state-propagation based language. Prolog+CG and Synergy incorporate two complementary models of processing: an inference-based processing (Prolog+CG) and an activation/propagation-based processing (Synergy). These engines are built on top of (use) the lower levels of Amine and they can be used separately or in combination. For instance, a Case-Base can be expressed as an Amine ontology and dynamic ontology engine can be used in conjunction with Prolog+CG to develop Case-Based Systems. In the two previous decades, intelligent processes (reasoning, learning, understanding, planning, etc.) are defined more precisely as Case-Based or Memory-Based processes; this is evident if we recall that intelligent processes are knowledge-based processes and knowledge is contained in and affected by memory. One important motivation for the development of Amine is to provide a platform for such a memory-based approach to intelligent processes. Amine ontology can play the role of the intelligent system's memory. Indeed, both terminological (definitional) and situational (cases) knowledge can be integrated in an Amine ontology. Beside these three complementary programming paradigms, Amine, provides three basic ontology processes: elaboration, elicitation and information retrieval.
4. Multi-Agents Systems level/layer: Amine provides agents and multi-agents systems (MAS) APIs that enable the construction of agents and multi-agents systems. Different types of agents (cognitive, reactive and integrated agents) and different types of MASs are being considered. Components of a typical agent in Amine platform can include: an ontology (with lexicons) that plays the role of the agent’s memory, structures and matching-based operations to represent and manipulate agent's knowledge structures, the dynamic ontology engine to perform an automatic and incremental integration in memory of the continuous stream of information that the agent receives from its environment, Prolog+CG for the inference-based processes, and Synergy for the activation and propagation-based processes.
The above four levels (layers) form a hierarchy: each level is built on top of and use the lower level. However, it is important to note that a lower level can be used by itself without the higher levels: kernel level/package can be used directly in any application, algebraic level/packages can be used directly too, etc. Among the goals (and constraints) that have influenced the design and implementation of Amine were the goal to achieve a higher level of modularity and independence between components.
This four-layers architecture of Amine was redefined, in Amine 4, as a five-layers architecture, where Applications constitutes the fifth layer (Figure 3).
Figure 3: Applications as a fifth layer in Amine Platform
Amine 5 architecture is refined and extended to seven-layers architecture (Figure 4): two new layers were added: Knowledge Base Layer and Memory-Based Inference and Learning Strategies Layer. See Knowledge Base Layer and Memory-Based Inference and Learning Strategies Layer for more detail about these two new layers.
Figure 4: Seven-Layers Architecture of Amine Platform
This seven-layers makes the intelligent system/agent architecture more clear and precise: it is more efficient to make explicit both the Ontology layer and the Knowledge Base layer, to separate between the two layers and to make explicit the strong relation between them. In the previous versions of Amine, the two layers were merged in one layer; an ontology in Amine may represent an ontology as well as a Knowledge Base.
The Knowledge Base layer is build upon the Ontology layer (Figure 4), and the two layers constitue the Memory of the intelligent system/agent. Knowledge structures and operations are build upon the Ontology/KB layers and constitute the algebraic layer. Memory-Based Inferences and Learning Layer provide (Knowledge) inferences and learning strategies that are build upon and use the three first layers. Amine's Memory-Based Inferences and Learning Layer provides some inference and learning strategies but it is far from being complete. Process/Programming/Engines layer is build upon the lower layers and provides two programming languages (Prolog+CG and Synergy) to define/develop different intelligent processes (problem solving, natural language processing, planning, etc.). Agent/Multi-Agent Systems layer is build upon the lower layers and incorporates the communication capability between agents.
Amine platform can be viewed as a basis for an Intelligent Virtual Machine; layers/levels of Amine cover most of the complementary components needed in the development of intelligent systems/agents with various capabilities. There is a strong cohesion and complementarity between Amine's layers.
However, it is not mandatory to use all the seven-layers of Amine in each application or in the development of any intelligent system/agent: as noted already, a layer requires lower layers, but it doesn't require the upper layers. Thus, a user may use all packages of Amine or it can use only those that are required for his/her application.
Agent architecture and Amine architecture
There is a strong affinity between our proposed Intelligent Agent achitecture (Figure 1) and the last version of Amine architecture (Figure 4): The kernel of an Intelligent Agent corresponds to the two first layers of Amine (Ontology and KB layers), and to the fourth layer. Note that the third layer of Amine (the algebraic layer) refeers to a fondamental component that is not explicit or even absent (or hide) in many intelligent agent architectures: Knowledge/Conceptual Structures and Operations. In conclusion, the first four layers of Amine allow agent's developer to design and implement the kernel of an intelligent agent.
Instead of providing specific implementation of cognitive processes (problem solving, natural language processing, planning, etc.), Amine provides two programming languages (PROLOG+CG and SYNEGY) that constitute its Programming/Engine/Process layer. With these languages, and the other components of Amine, user (developer of intelligent agents) can define and implement the agent's cognitive processes.
Amine APIs Specification
Amine APIs Specification can be found here.
Genericity in Amine
One important feature of Amine is the effort to maximize the genericity of the platform:
a. the kernel (multi-lingua ontology) is generic in the sense that Amine ontology is an integration of various kinds of ontologies (see Ontology for more detail on this point) and an ontology is independent from any specific description scheme;
b. the algebraic level is generic in the sense that it supports generic structures (structures with variables) and generic binding context. The algebraic level is also generic in the sense that it is "open" to Java; it provides interfaces (AmineObject and Matching) that allow the integration of new structures to Amine.
c. the dynamic ontology engine is generic in the sense that it considers various types of information. Prolog+CG is generic in its integration of Amine lower levels, in its object extension of Prolog and its "openess" to Java, etc.
Amine's genericity will be enhanced in the future.
The "proximity" of the conceptual/specification level and the implementation level
Great efforts have been made to significantly reduce the "gap" between the conceptual/specification level and the implementation level of Amine platform. This is reflected in the architecture of the source code, in the packages and classes, in the naming and especially in the APIs that provide several methods that "bring" the conceptual level to the programming level. Great efforts have been made to produce a very easy and readable code, very close to the conceptual and specification levels of the platform. In this way, the code can be easily enhanced, debugged, updated, modified and extended. A quick look at Amine's APIs and related tests will give the reader an idea about what we mean by this important feature.
From Amine Platform 1.0 to Amine Platform 2.0
Amine Platform 2.0 is a complete revision and refinement of the first version, both at the conceptual and implementation levels. At the conceptual level, concepts such as language, lexicon, conceptual structure, ontology, descriptions, and binding context are made clearer in 2.0 than in 1.0. The genericity of Amine is specific to the new version. Also, structures and operations of Amine have been redefined and extended. GUIs have also been modified and refined. And in addition to the three GUIs presented in the previous version (LexiconsOntology GUI, CGNotations and CGOperations GUIs), this new version presents Amine Platform Suite GUI (Figure 5); a frame that visualizes the whole components of the platform and provides a direct access to its various GUIs. It also gives, via the Help menu, an access to the APIs specification, the documentation and the URL to the new Amine Web Site.
Unlike the previous Amine Web Site version, the current web site is much more informative and well structured; more documentation (in addition to the APIs specification) is available on the new web site.
Figure 5: Amine 2.0 Platform Suite
Note: Engines and MAS layers will be released in the next versions.
At the implementation level, the code has been completely rewritten to satisfy many goals: clarity, readability, organization, and proximity with the conceptual level (the code reflects closely the conceptual and specification levels). The result is a better naming, definition, organization, and documentation of methods, classes and packages. The goal of this effort is to offer an open source that can be easily used, modified and extended.
The release of Amine Platform 2.0 contains the source code, the compiled code, tests done on the APIs, samples (currently, the current version contains only a subdirectory for ontology samples), documentations (i.e., the APIs specification which has been enhanced), the deployment of Amine on various platforms, and the new web site.
Inside Amine Platform 2.0: a quick overview of Amine packages
We provide a quick overview of Amine Platform 2.0 packages. More details can be found in Implementation issues and in the pages associated to the different components of Amine. Recall that engines and MAS layers are not included in the current version.
The organization of Amine's source code reflects its specification; Amine's source code contains packages that correspond to Amine levels: kernel package for the kernel level, util package for the algebraic level, guis package for Amine GUIs, and test and samples packages. In the next versions, we will provide the engines and the Multi-Agents Systems packages.
The kernel package is composed of two packages: lexicons and ontology packages, the former for Lexicon API and the latter for Ontology API. The ontology package includes classes that define the four types of Conceptual Structures (CS) used in Amine Platform: Type, RelationType, Individual and Situation. The Canon CS is integrated in the Type CS (a type can have a definition and/or a canon). Ontology API is partitioned over all the classes of the ontology package.
As stressed above, the kernel package (lexicons and ontology packages) is independent from the other packages and it can be used alone.
Amine's source code contains also a package called util (like the package with the same name in Java) where the basic structures of Amine Platform are defined. An object in Amine can be an elementary Java object (Integer, Double, Boolean, and String), an elementary Amine object (AmineInteger, AmineDouble, AmineBoolean, and Identifier, a CS), an Amine collection object (AmineSet and AmineList), and an Amine complex object (Term, Concept, Relation, and CG). Also, any Java Object that implements AmineObject and Matching interfaces becomes an Amine object and can be used as a component of an Amine structure.
The common operations (clear(), clone(), toString()) are specified in AmineObject interface. Another set of common operations are matching-based operations (match(), equal(), unify(), subsume(), maximalJoin(), and generalize()). These operations are specified in the Matching interface. The two interfaces are implemented by all Amine structures.
util package includes a package, called cg, for the Concept, Relation and CG structures and their associated operations. util includes also a parserGenerator package which concerns parsing and textual formulation of all Amine structures.
Amine's source code also contains a guis package which includes packages for the current GUIs of Amine Platform (lexiconsOntology GUI, cgNotations GUI, cgOperations GUI, and aminePlatform GUI). The util Package in guis contains classes that are used by the various GUIs.
Further details on the implementation are provided in implementation issues and in the pages associated to the different components of Amine Platform.
From Amine 2.0 to Amine 3.0
See release of Amine 3 for the difference between Amine 3 and Amine 2.
From Amine 3.0 to Amine 4.0
Amine 4.0 includes Ontology Graphic Editor, Dynamic Programming in Synergy, MAS layer and several other enhancements in comparison with Amine 3.0. See release of Amine 4 for more detail.
From Amine 4.0 to Amine 5.0
Amine 5.0 introduces two new layers: Knowledge Base Layer and Memory-Based Inference and Learning Strategies layer. It introduces also Rule as a new Conceptual Structure, beside Definition, Canon, Individual and Situation. Ontology and KB can contain Rules.
Dynamic Integration Process has been refined in different "subprocesses", taking into account the variety of Conceptual Structures and of ontologies and of KB that can be constructed: definition integration process, situation integration process, rule integration process, heterogenous integration process.
Also, Amine 5.0 makes clear the three modes of integration process: classification-based integration process, generalization-based integration process, and information retrieval integration process.
See release of Amine 5.0.
From Amine 5.0 to Amine 6.0
Amine 6.0 brings a lot of changes and extensions. See Amine 6.0 release for more detail. Figure 6 shows the updated form of Amine Suite.
Figure 6: Amine 6.0 Platform Suite
From Amine 6.0 to Amine 8.5
See release archives.
Various types of intelligent systems can be developed using Amine Platform: the three first layers/levels provide tools to develop Knowledge-Based and Ontology-based systems. With Prolog+CG, intelligent and intentional systems can be developed also (like inference-based systems, natural language processing systems, and planning systems). With inference and learning strategies layer, Amine Platform provides the possibility to build intelligent systems with memory-based inference and learning capabilities: indeed, dynamic ontology and KB correpond in this context to memory and the dynamic ontology/KB engines are learning processes that can be extended to integrate other learning strategies like analogy, abduction and explanation based learning. Case-Based Systems can be developed using for instance Prolog+CG and the other lower layers. Activation-based systems can be developed using Synergy language which is an activation/propagation based language suited for data/event and result/goal driven computations. Last, Multi-Agent Systems layer/level, that can exploits the lower layers/levels of Amine, is intended for the development of different types of agents and of multi-agents systems.
Being implemented in Java, Amine can be viewed as an extension of the Java Virtual Machine (JVM) to Java Intelligent Virtual Machine, in order to implement easily various types of intelligent systems/agents.
PART 1: ONTOLOGY and KNOWLEDGE BASE LAYERS
6. Ontology GUI
PART 2: ALGEBRAIC LAYER: CONCEPTUAL STRUCTURES & OPERATIONS
PART 3: MEMORY-BASED INFERENCE AND LEARNING STRATEGIES LAYER
PART 4: PROCESS/PROGRAMMING/ENGINE LAYER
18. SYNERGY language
PART 5: AGENT AND MULTI-AGENT SYSTEMS LAYER
PART 6: APPLICATIONS LAYER