We can take many kinds of approaches to the problem. At first sight, it seems a kind of planning or scheduling problem in an artificial intelligence sense.
Although determining a plan for an individual user can be executed by planning, social coordination cannot be handled well by conventional planning and scheduling techniques that lack real-time response.
Genetic algorithm (GA) or reinforcement learning works well because of its ability to generating new, flexible plans, but it also lacks real-time response.
Stochastic distribution, e.g., CSMA/CD used in Ethernet (IEEE802.3) for packet collision avoidance, works fast, but it cannot generate good plans because it doesn’t take the user’s intention or preferences into consideration.
Another approach is to introduce some kind of market or auction mecha-nism by preparing a kind of bulletin board where a part of plan linked to users’
intention or preferences is exchanged among users. Market or auction mecha-nisms reflect an individual user’s model and generate good plans faster than planning, GA, and reinforcement learning, but it is slower than stochastic distribution.
To summarize the candidates for social coordination mechanism, each candidate has merits and demerits as follows:
(1) Combinatorial Optimization. Coordination problem can be formalized as combinatorial optimization problem (e.g., Lawler et al., 1985) in many Figure 3. Plans and Congestion in Resource Space
User 3
User 2 User 1
Spatial Segment
Temporal Segment
S1 S2 S3 …. Sn-1 Sn
t1 t2
tm-1 tm
Future ... t0
cases, which can generally be solved by genetic algorithm (Goldberg, 1989). This approach can give the most optimal solution, but real-time response is difficult.
(2) Stochastic Distribution. Stochastically distributing resources among users is a time-efficient approach (e.g., Floyd et al., 2001), which can be analyzed by a queuing network (e.g., Chao et al., 1999). The solutions obtained by this approach usually lack accuracy, i.e. obtained solutions are far from optimal ones.
(3) Market Mechanism. Methods based on market mechanism (see Wellman et al., 2001; Prado & Wurman, 2002) can reflect the flexibility of users’ motivations and intentions, and it keeps real-time response.
Basically, fluctuations are observed in market mechanisms, by which the behavior of the whole system can become unstable.
(4) Planning and Scheduling. Conventional AI planning and scheduling (e.g., Miyashita, 2000) are flexible methods that can control spatial and/
or temporal complexity and the accuracy of the solution by using heuris-tics. Unfortunately, preparing good heuristics for all kinds of problems is nearly impossible.
We are now designing an algorithm for mass user navigation based on the generation, connection, and evaluation of plans, with stochastic distribution and exchange in market and auction mechanism. The basic idea is that, first, we generate element plans for individual users, and then we connect the plans to increase both types of utilities. If congestion occurs in this process, we modify each user’s plan by stochastically distributing its elements in the resource place.
This algorithm itself seems to work well and fast, but it does not generate good candidates because it does not take the user’s intention or preferences into consideration. We then introduce an exchange mechanism, by using a market-like bulletin board, in order to decrease the number of the applications of stochastic distributions.
CONCLUSION
In this chapter, we have proposed the concept of social coordination in daily life, which is a mutual concession mechanism for social resources, e.g., space, time, and reservations, through automatic negotiation among software agents rather than through the explicit and verbal communication of human users.
We have also proposed a new kind of architecture, called CONSORTS, for ubiquitous agents in which mass user support services are provided in addition to conventional personal supports.
As an example of social coordination, we have proposed, formalized and analyzed mass user navigation. Although mass user navigation seems to be a planning or scheduling problem at first sight, we have pointed out that conventional problem-solving mechanisms, such as planning, scheduling, GA, reinforcement learning, or stochastic distribution itself, do not work well. To solve the problem, we have proposed a method based on the generation, connection and evaluation of plans, with plan modifications by stochastic distribution and market/auction mechanism, i.e., a kind of bulletin board where users’ intentions and preferences are exchanged among users.
Social coordination is not a part of social collaboration. It requires real-time response, although it cannot necessarily generate the best solutions. Real-time response does not seem to be crucial in the theme park problem, but it is really important in other applications, such as social coordination in traffic control, because we do not have much time for decision making in traffic and for navigation guidance when we drive a car. In addition, if we can reduce the amount of traffic in a city or country by just one percent, it will bring much benefit to the economy and environment.
Social coordination is working as an underlying mechanism in our daily lives. Our intention is to enhance such mutual concession mechanisms in a sophisticated way by using software agent technologies. Because this research is just beginning, we will examine and refine the definition of the problem and the algorithm to solve it, first by multi-agent simulation and later by applying it to real situations.
ACKNOWLEDGMENT
The author would like to thank Hidenori Kawamura, Akio Sashima and Noriaki Izumi for their comments and suggestions on social coordination and architecture for ubiquitous agents.
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