Created: 04/15/99 :: Updated:2/19/03 :: Visitors: :: © A. Drogoul 1993-2003 ::

Content

This page provides brief primers of concepts or notions I am developing or using. The definitions that follow are often taken from papers I wrote. More complete information can be found on the Papers and Research Activities pages.

Pervasive Intelligence (Feb. 2003)

Pervasive computing, also known as ubiquitous computing, is a concept that relies on the following predictions:

These devices will be the components of a kind of computing infrastructure that will radically differ from the ones we know today. As a matter of fact, these systems will:

We want these systems, of course, to behave in a predictable and user-friendly way, but we clearly lack, today, the tools or methods that would allow us to design, program and even use them. This is due to their physical and functional distribution, mobility and heterogeneity, which render them far more complex than today’s networked computing systems. These issues are not likely to be addressed in a simple way and will require the development of new computing paradigms. Most of them will however derive from the concepts, tools, and methods developed in the field of DAI. This domain is one of the few to naturally deal with the “decentralized mindset” required for apprehending these systems. However, this will also require it to re-think many of its underlying models. As a matter of fact, most of the (theoretical or empirical) works on multi-agent systems rely on:

Designing these “new” MAS will certainly require us to consider alternative sources of inspiration, other than economy or sociology. One promising direction (which I call Pervasive Intelligence, PI) is to view and design them as “ecosystems of physical agents” (heterogeneous, open, dynamical, and massive group of interacting agents), organized after “biological”, “physical” or “chemical” principles. Two domains of research illustrate this direction: reactive multi-agent systems and amorphous computing. The first one studies complex, self-organized, situated systems that rely on biological metaphors of communication and organization. The second one, which draws its inspiration from chemistry, tries to develop engineering principles to obtain coherent behavior from the cooperation of large numbers of unreliable computing parts connected in irregular and time-varying ways. Pervasive Intelligence would be an ideal combination of these two domains, synthesized in the following question: “How to obtain coherent and predictable behaviors from the cooperation of large numbers of situated, heterogeneous agents that are interacting in unknown, irregular, and time-varying ways?”. Answering this question would allow us to design truly intelligent, adaptive and autonomous pervasive computing systems.

See also this paper.

Participatory Design of ABS (Feb. 2003)

One of the most difficult parts, when designing Multi-Agent Based Simulations, is the formalization of the behaviors of the agents after they have been defined (not formally) by the thematician. This extraction of knowledge may require several iterations before it satisfies all the actors involved. The recent rise of participatory simulations has opened a whole new field of research in this area, by allowing experts and non-experts to interactively define these behaviors, through role-playing games or by being immersed as “human agents” in a running simulation (see for instance this tool). The resulting behaviors, however, are still hand-coded by computer scientists.

Autonomous agents can be advantageously used in this process. They may, for example, play a twofold role (see figure): that of computational abstractions for implementing the simulation and that of “assistant-like” or “profiling” agents dedicated to an user or an expert. If they are provided with adequate learning capabilities, they may then autonomously adjust or acquire their behavior through repeated interactions with (or observations of) their “user”. This kind of learning procedures are being actively investigated in many subfields of AI under different names: Adjustable Autonomy, Learning by Imitation , Learning by Demonstration, Social Learning, Interactive Situated Learning, etc. One of their main advantages is that the agent may learn its behavior “in situation” and in a progressive way, and that the expert is really involved in this process. The downside is that it requires, until now, domain-dependant formalisms for representing actions and contexts, which means that generic architectures will not be available before long. However, researches on this subject have already been launched in several simulation projects, and I am confident that machine learning, through agent-based simulation, will be part of any modelers’ toolkit in a few years.