The Reactive Layer

2.4 The Reactive Layer

The realization of the reactive layer differs from one domain to another. It depends on the actions the agent should execute and on the kind of sensors providing information about the environment. In our transport domain there are only few sensors. The road trains might be equipped with an GPS-System which returns the position of the truck. Upon the actual time and the position of the road train the agent is able to check its plan and if there is time enough to serve all orders correctly. In other domains there are much more sensors thinkable, e.g. if the agent represents a robot, it might have several infrared sensors etc. So this shows that the structure of the abstraction levels really depends on the domain of the agents. In our domain the abstraction hierarchy is divided into three levels. First, there is a generic reactive layer which observes the status of the agent. It has to control whether the communication works and if all sensors and actors are in order. This level is responsible for technical correctness of the hardware and the technical surroundings of the agent.

Generic com. Generic com. layer layer

Transp. com. layer Transp. com. layer

Transp. prot. Transp. prot.

1..n

layer layer

Com. layer Com. layer

Holon Com. layer Holon Com. layer

Ship. Com. layer Ship. Com. layer

Generic planning Generic planning layer layer

Generic KB layer Generic KB layer

Bid computation Bid computation

Driving time Driving time

module module

module module

Transp. planning Transp. planning layer layer

Roadmap module Roadmap module

Planer - container Planer - container

Planer - holon Planer - holon

Planer - driver Planer - driver

Planer - general Planer - general

Transp. KB layer Transp. KB layer

Generic reactive Generic reactive layer layer

Simulation Simulation module module

Transp. reactive Transp. reactive layer layer

planexecution planexecution

plan observing plan observing

layer layer

layer layer

Figure 3.

The object hierarchies within the agent architecture

The next level creates a correlation between reactive and planning Analogous to the planning layer there is another level below which layer. This level of the reactive layer has to check if all entries in

can be seen as a module level. It contains some algorithms for the knowledge base are still okay, and if not, it has to initiate some

quick replanning to get out of critical situations and some modules action which will rectify the knowledge base. However the

for a kind of simulation. Simulation is necessary to make a processing of the plans of the agent differs from the knowledge

visualisation of the activities on the one hand and on the other handling. If a fault in the future of the plan is noticed, say that the

hand it is essential to project the situation to the future to see what execution of the plan would be impossible if the agent does not

can happen. From a software developer’s point of view the life modify its plan, this level has to generate a call to the planning

time of these modules is short. So it is necessary to change this layer of the agent to build a new plan on the base of the current

modules without a lot of work and without changing the whole sensor data and knowledge base.

layer.

The third level of the reactive layer is responsible for the correct execution of the actions which are described in the plans of the agent. Therefore this level has to be able to communicate with the relevant hardware components to execute the tasks at hand. This level is responsible for short time observation whereas the level above is considered to do a long time observation.

Proceedings of the ECAI 2000 Workshop on Agent Technologies and Their Application Scenarios in Logistics

2.5 The Knowledge Base

REFERENCES

To build a generic knowledge base for all kind of scenarios is not BFV99: H.-J. Bürckert, K. Fischer, and G. Vierke. Holonic easy because knowledge base systems are very complex and cannot

Fleet Scheduling with TeleTruck . In Proceedings of the

be divided in independent parts. Hence, we decided to restrict to two abstraction levels.

Second International Conference on Computing Anticipatory Due to the fact that most knowledge bases are build up on

Systems (CASYS'98), 1999

databases, the highest knowledge base level mainly prepares interfaces between database and agent layers. If only a standard

BV99: H.-J. Bürckert and G. Vierke. Simulated Trading SQL-database has to be included, this level should provide some

Mechanismen für Speditionsübergreifende methods which enables the agent to request information from the

Transportplanung . In H. Kopfer and C. Bierwirth, editors, database using the SQL syntax.

In our domain there is no ‚active‘ knowledge base needed which Logistik Management - Intelligente I+K Technologien. has to draw inferences about the knowledge. The knowledge base

Springer-Verlag, 1999.

layer has only to be able to store data about the resources of the agents and it has to save the plan information if the system is going

FF94: T. Finin, R. Fritzson. KQML – A Language and down. From an abstract point of view the knowledge base is just a

Protocol for Knowledge and Information Exchange . data pool for an agent. However, this level includes all interfaces to

Proceedings of the 13 th International Distributed Artificial maybe different database systems and to systems which can hold

persistent data.

Intelligence Workshop, 1994.

In the next lower abstraction level there are algorithms to draw

inferences. This level includes the mechanism which constitutes a FVB99: P. Funk, G. Vierke, and H.-J. Bürckert. A Multi- real knowledge base system.

Agent Systems Perspective on Intermodal Transport Chains. In H. Kopfer and C. Bierwirth, editors, Logistik Management - Intelligente I+K Technologien. Springer-

3 CONCLUSION

Verlag, 1999.

We have described an approach that allows to design and implement multi agent systems in a simple and convenient

GSV99: C. Gerber, J. Siekmann, and G. Vierke. Holonic manner. By abstracting from several existing agent systems

Multi-Agent Systems . Research Report RR-99-03, DFKI, for transportation purposes, we have constructed a generic

1999.

agent model that in turn serves to derive new specific agents in a straightforward manner and that allows to save

Mül96: J. P. Müller. The Design of Intelligent Agents – a redundant implementation effort. The model combines a

Layered Approach . Volume 1177: Lectures notes in sophisticated agent architecture with the holonic agent

artificial intelligence, Springer, 1996. design paradigm. Our future work aims at implementing a computer aided

Sea69: J. R. Searle: Speech Acts. Cambridge University software engineering tool that supports the automated

Press, 1969.

generation of agents independently from programming languages and hardware platforms. We consider our generic

Wei99: G. Weiss: Multiagent Systems – A Modern agent model being an appropriate basis for this purpose.

Approach to Distributed Artificial Intelligence. MIT Press,1999.

Proceedings of the ECAI 2000 Workshop on Agent Technologies and Their Application Scenarios in Logistics

MULTIDIMENSIONAL UTILITY VECTORS IN