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Formalisms for representing communication in agent theory tend to be based on speech act theory Wooldridge and Jennings, 1995; Ferber, 1999; Weiss; 1999; Wooldridge,
2002, as originated by Austin in 1962, and further developed by Searle in 1969 Wooldridge and Jennings, 1995. The key principle of speech act theory is that
communicative utterances are actions, in the same sense that physical actions are. They noticed that a certain class of natural language utterances or speech acts had the
characteristics of actions, in the sense that they change the state of the world in a way analogous to physical actions. They observed that most things people say are not simply
propositions that are true or false, but are performatives that succeed or fail. Since the early 1990s, speech act theories have directly informed and influenced a number of
languages that have been developed for agent communication, such as KQML and ACL. In KQML and ACL, each message has a performative a class of the message
and a number of parameters to describe the format of the message sender, receiver, content, etc.. The most important differences between these two languages are in the
collection of performatives they provide.
2.5.5 Application of MAS in Scheduling
The advantages of MAS have led to increasing interest in the application of MAS in different fields of research, including scheduling. The advantages can be explained by
the following points Wooldridge and Jennings, 1995; Nwana, 1996; Ferber, 1999; Oliveira
et al., 1998; Weiss, 1999; Shen et al., 2001; Wooldridge, 2002: • Robustness and reliability against failures. MAS architecture is distributed
where it allows fast detection and recovery from failures, and the failure of one or several agents does not necessarily make the overall system ineffective.
• Scalability and flexibility. Because MAS is an open and dynamic structure, the system can be adapted to an increased problem size by adding new agents, and
without affecting the functionality of the other agents. • Computational efficiency. Agents can operate asynchronously and in parallel,
which can result in increased overall speed. • Clarity of design and reusability. Individual agents can be developed separately
and it may be possible to reuse agents in different application scenarios.
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Moreover, the overall system can be tested and maintained, and reconfigured more easily.
• Costs. It may be much more cost-effective than a centralised system, since it could be composed of simple subsystems of low unit cost.
In scheduling problems there have been many efforts to apply MAS, such as supply chain scheduling management Julka et al., 2002, Wagner et al., 2003; logistics
management and scheduling Karageorgos et al., 2003; airline scheduling Langerman
and Ehlers, 1997; meeting scheduling Lee and Pan, 2004; processor scheduling Lopez-Ortiz and Schuierer, 2004; scheduling for patient tests in hospital laboratories
Marinagi et al., 2000; scheduling of robotic explorers in space technology Muscettola et al., 1998; event scheduling Riekki et al., 2003; parallel computing Seredynski,
1997 and manufacturing scheduling Parunak, 1998, 2000; Maturana and Norrie, 1997; Tharumarajah and Bemelman, 1997; Brennan and Norrie, 1998; Gou
et al., 1998; Maturana
et al., 1999; Rabelo et al., 1998; Shen and Norrie, 1999; Sousa and Ramos, 1999; Jia
et al., 2004. However, to the best of our knowledge, there is no literature about the application of
MAS to bus crew scheduling. In bus crew scheduling, when UE happen, one way to deal with them is quick rescheduling, which is necessary to prevent cancelled journey or
bus delay. A MAS is considered suitable to support this rescheduling because agents can dynamically adapt their behaviour to changing requirements and they can find quick
solutions via negotiations and cooperation between them. Speed is an important issue when it comes to day-to-day operation management. In MAS, the computational effort
is dramatically reduced because each agent knows its attributes and tries to solve the problem through negotiation with relevant agents not with every agent, and each agent
can also capture requirements and preferences of its owner. For example, a crew agent is able to accommodate crew preferences – such as preferred driving time of the day.
2.6 Research Questions
