Constructionist Design Methodology for Interactive Intelligences

By Thorisson, Kristinn R.; Benko, Hrvoje et al. | AI Magazine, Winter 2004 | Go to article overview

Constructionist Design Methodology for Interactive Intelligences


Thorisson, Kristinn R., Benko, Hrvoje, Abramov, Denis, Arnold, Andrew, Maskey, Sameer, Vaseekaran, Aruchunan, AI Magazine


The creation of emboIdied humanoids and broad AI systems requires integration of a large number of functions that must he carefully coordinated to achieve coherent system behavior. We are working on formalizing a methodology that can help in this process. The architectural foundation we have chosen for the approach is based on the concept of a network of interacting modules that communicate via messages. To test the design methodology we chose a system with a human user that interacts in real time with a simulated human in an augmented-reality environment. In this article we present the design methodology and describe the system that we built to test it.

Newell (1990) urged for the search of unified theories of cognition, (1) and recent work in AI has increasingly focused on integration of multiple systems (compare with Simmons et al. 2003, McCarthy et al. 2002, and Bischoff and Graefe 1999). Unified theories necessarily mean integration of many functions, but our prior experience in building systems that integrate multiple features from artificial intelligence and computer graphics (Bryson and Thorisson 2000, Lucente 2000, Thorisson 1999) has made it very clear that such integration can be a challenge, even for a team of experienced developers. In addition to basic technical issues--connecting everything together can be prohibitive in terms of time--it can be difficult to get people with different backgrounds, such as computer graphics, hardware, and artificial intelligence, to communicate effectively. Coordinating such an effort can thus be a management task of a tall order; keeping all parties synchronized takes skill and time. On top of this comes the challenge of deciding the scope of the system: what seems simple to a computer graphics expert may in fact be a long-standing dream of the AI person, and vice versa.

Several factors motivate our work. First, a much-needed move towards building on prior work in AI to promote incremental accumulation of knowledge in creating intelligent systems is long overdue. The relatively small group working on broad models of mind, the researchers who are bridging across disciplines, need better ways to share results and work together and to work with others outside their fields. To this end our principles foster reusable software components through a common middleware specification (2) and mechanisms for defining interfaces between components. Second, by focusing on the reuse of existing work, we are able to support the construction of more powerful systems than otherwise possible, speeding up the path towards useful, deployable systems. Third, we believe that to study mental mechanisms they need to be embedded in a larger cognitive model with significant breadth to contextualize their operation and enable their testing under boundary conditions. This calls for an increased focus on supporting large-scale integration and experimentation. Fourth, bridging across multiple functions in a single, unified system increases researchers' familiarity and breadth of experience with the various models of thought to date--as well as new ones. This is important--as are in fact all of the above points--when the goal is to develop unified theories of cognition.

Inspired to a degree by the classic LEGO bricks, our methodology--which we call a constructionist approach to AI--puts modularity at its center: Functions of the system are broken into individual software modules, which are typically larger than software classes (that is, objects and methods) in object-oriented programming but smaller than the typical enterprise application. The role of each module is determined in part by specifying the message types and content of the information that needs to flow between the various functional parts of the system. Using this functional outline we then define and develop, or select, components for perception, knowledge representation, planning, animation, and other desired functions. …

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