Artificial intelligence systems integration


The core idea of Artificial Intelligence systems integration is making individual software components, such as speech synthesizers, interoperable with other components, such as common sense knowledgebases, in order to create larger, broader and more capable A.I. systems. The main methods that have been proposed for integration are message routing, or communication protocols that the software components use to communicate with each other, often through a middleware blackboard system.
Most artificial intelligence systems involve some sort of integrated technologies, for example the integration of speech synthesis technologies with that of speech recognition. However, in recent years there has been an increasing discussion on the importance of systems integration as a field in its own right. Proponents of this approach are researchers such as Marvin Minsky, Aaron Sloman, Deb Roy, Kristinn R. Thórisson and Michael A. Arbib. A reason for the recent attention A.I. integration is attracting is that there have already been created a number of simple A.I. systems for specific problem domains, and that integrating what's already available is a more logical approach to broader A.I. than building monolithic systems from scratch.

Why integration?

The focus on systems integration, especially with regard to modular approaches, derive from the fact that most intelligences of significant scales are composed of a multitude of processes and/or utilize multi-modal input and output. For example, a humanoid-type of intelligence would preferably have to be able to talk using speech synthesis, hear using speech recognition, understand using a logical mechanism, and so forth. In order to produce artificially intelligent software of broader intelligence, integration of these modalities is necessary.

Challenges & solutions

Collaboration is an integral part of software development as evidenced by the size of software companies and the size of their software departments. Among the tools to ease software collaboration are various procedures and standards that developers can follow to ensure quality, reliability and that their software is compatible with software created by others. However, collaboration in fields of A.I. has been lacking, for the most part not seen outside of the respected schools, departments or research institutes. This presents practitioners of A.I. systems integration with a substantial problem and often causes A.I. researchers to have to 're-invent the wheel' each time they want a specific functionality to work with their software. Even more damaging is the "not invented here" syndrome, which manifests itself in a strong reluctance of A.I. researchers to build on the work of others.
The outcome of this in A.I. is a large set of "solution islands": A.I. research has produced numerous isolated software components and mechanisms that deal with various parts of intelligence separately. To take some examples:
With the increased popularity of the free software movement, a lot of the software being created, including A.I. systems, is available for public exploit. The next natural step is to merge these individual software components into coherent, intelligent systems of a broader nature. As a multitude of components have already been created by the community, the most accessible way of integration is giving each of these components an easy way to communicate with each other. By doing so, each component by itself becomes a module which can then be tried in various settings and configurations of larger architectures.
Many online communities for A.I. developers exist where tutorials, examples, and forums aim at helping both beginners and experts build intelligent systems. However, few communities have succeeded in making a certain standard or a code of conduct popular to allow the large collection of miscellaneous systems to be integrated with any ease. Recently, however, there have been focused attempts at producing standards for A.I. research collaboration, is an online community specifically created to harbor collaboration in the development of A.I. systems. The community has proposed the OpenAIR message and routing protocol for communication between software components, making it easier for individual developers to make modules instantly integrateble into other peoples' projects.

Methodologies

Constructionist Design Methodology

The Constructionist design methodology is a formal methodology proposed in 2004, for use in the development of cognitive robotics, communicative humanoids and broad AI systems. The creation of such systems requires the integration of a large number of functionalities that must be carefully coordinated to achieve coherent system behavior. CDM is based on iterative design steps that lead to the creation of a network of named interacting modules, communicating via explicitly typed streams and discrete messages. The OpenAIR message protocol was inspired by the CDM and has frequently been used to aid in the development of intelligent systems using CDM.
One of the first projects to use CDM was , an embodied, graphical agent visualized through augmented reality which could communicate with human users and talk about objects present in the user's physical room. Mirage was created by Kristinn R. Thórisson, the creator of CDM, and a number of students at Columbia University in 2004. The methodology is actively being developed at Reykjavik University.

Tools

OpenAIR Protocol

OpenAIR is a message routing and communication protocol that has been gaining in popularity over the past two years. The protocol is managed by , and is described on their site in the following manner:
"OpenAIR is a routing and communication protocol based on a publish-subscribe architecture. It is intended to be the "glue" that allows numerous A.I. researchers to share code more effectively — "AIR to share". It is a definition or a blueprint of the "post office and mail delivery system" for distributed multi-module systems. OpenAIR provides a core foundation upon which subsequent markup languages and semantics can be based, e.g. gesture recognition and generation, computer vision, hardware-software interfacing, etc.; for a recent example see ."
OpenAIR was created to allow software components that serve their own purpose to communicate with each other to produce large scale, overall behavior of intelligent systems. A simple example would be to have a speech recognition system, and a speech synthesizer communicate with an expert system through OpenAIR messages, to create a system that can hear and answer various questions through spoken dialogue. CORBA is an older but similar architecture that can be used for comparison, but OpenAIR was specifically created for A.I. research, while CORBA is a more general standard.
The OpenAIR protocol has been used for collaboration on a number of A.I. systems, a list can be found on the . Psyclone is a popular platform to pair with the OpenAIR protocol.

Psyclone AIOS

is a software platform, or an AI operating system, developed by for use in creating large, multi-modal A.I. systems. The system is an implementation of a blackboard system that supports the OpenAIR message protocol. Psyclone is available for free for non-commercial purposes and has therefore often been used by research institutes on low budgets and novice A.I. developers.

Elvin

Elvin is a content-based router with a central routing station, similar to the Psyclone AIOS.

OAA

The OOA is a hybrid architecture that relies on a special inter-agent communication language – a logic-based declarative language which is good for expressing high-level, complex tasks and natural language expressions.

CORBA

The Common Object Request Broker Architecture is a standard that enables software components written in multiple computer languages and running on multiple computers to interoperate. CORBA is defined by the Object Management Group. CORBA follows similar principles as the OpenAIR protocol, and can be used for A.I. systems integration.

MOSID

The Messaging Open Service Interface Definition is an O.K.I. specification which provides a means of sending, subscribing and receiving messages. OSIDs are programmatic interfaces which comprise a Service Oriented Architecture for designing and building reusable and interoperable software.

Examples of Integrated Systems