Soft Computing

The two major problem-solving technologies include:

1. hard computing.
2. soft computing.

 Hard computing deals wirh. precise models where accurate solmions are achieved quickly. On the other hand, soft computing deals with approximate models and gives solution to complex problems. The t<.yO problem-solving technologies are shown in Figure 1·2. Soft computing is a relatively new concept, the rerm really entering general circulation in 1994. The term "soft computing" was introduced by Professor Lorfi Zadeh with the objective of exploiting the tolerance for imprecision, uncenaincy and partial truth tO achieve tractability, robustness, low solution cost and better rapport with realicy.

 The ultimate goal is m be able to emulate fie human mind as closely as possible. Soft compuring involves parmership of several fields, the mosr imponam being neural nerworks, and FL. Also included is the field of probabilistic reasoning, employed for its uncertaincy control techniques. However, this field is nor examined here. Soft computing uses a combination of GAs, neural nerworks and FL.

 A hybrid technique, in fact, would inherit all the advantages, but won't have the less desirable features of single soft computing componems. It has to possess a good learning capacicy, a better learning time than that of pure GAs and less sensitivity to the problem of local extremes than neural nerworks. In addition, it has m generate a fuzzy knowledge base, which has a linguistic representation and a very low degree of computational complexity. 

An imponam thing about the constituents of soft computing is that they are complementary, not itive, offering their own advantages and techniques to pannerships to allow solutions to otherwise unsolvable problems. The constituents of soft computing are examined in turn, following which existing applications of partnerships are described. "Negotiation is the communication process of a group of agents in order to reach a mutually accepted agreement on some matter." 

This definition is typical of the research being done into negotiation and ordination in relation to software agents. It is an obvious necessity that when multiple agents interact, they will be required to co-ordinate their efforts and attempt to son our any conflicts of resources or interest.

 It is important to appreciate rhar agents are owned and conrrolled by people in order to complete tasks on their behalf. An exampl! of a possible multiple-agent-based negotiation scenario is the competition between phone call providers.