The word semantics means the study of meaning. It typically focuses on the relation between the signifers, such as words, phrases, signs and symbols, and what they stand for. Linguistic semantics is defined as the study of meanings that humans use language in expression. Other types of semantics include the semantics of programming languages, formal logics, and semiotics.The word semantic itself denotes a range of ideas, from the fashionable to the highly technological. It is frequently used in ordinary language to denote a problem of understanding that comes down to word collection or connotation. This problem of understanding has been the subject matter of many formal investigations, over a long period of time, most especially in the field of formal semantics. In linguistics, it is the study of interpretation of signs or symbols as used by agents or communities within particular situation and contexts. Within this observation, sounds, facial terminology, body language, phonemics have semantic (significant) content, and each has several branches of study. For instance in written language, such things as paragraph structure and punctuation have semantic content; in other form of languages, there is other semantic content .As mentioned above. the official study of semantics intersects with many other fields of inquiry, including lexicology, syntax, pragmatics, etymology etc though semantics is a well-defined field in its own context, but is often with artificial properties. In language philosophy, semantics and reference are related fields. Further related fields include philology, communication, and semiotics. With the interrelationship between them the formal study of semantics is therefore multifarious in nature. Semantic is in contrast with syntax, the study of the combinatory of units of a language (with no reference to their meaning). In the scientific vocabulary semantics is also known as semasiology.
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Introduction
One of the major reasons for agent abstraction importance in engineering purposes is that it allows “necessary” complication and disability of today’s computer systems to be dealt with better than before. Also the most conventional perspective of agents that intelligent software components, acting on an erratic environment. The typical solution to this problem is to employ a black-box approach, e.g., describing the agent behavior solely by means of its inputs and outputs. Modeling agent behavior within MAS introduces taxing issues, since
both the agent internal behavior and interactive behavior are concerned. This is the problem that is addressed by formal semantics of agent communication languages (ACL) (Kone, Shimazu, and Nakajima 2000).This relationship between an agent abstract structural design and the specification of ACL semantics can be highlighted by considering the case of current semantics for ACLs such as, FIPA ACL (FIPA 2000) and KQML (ARPA Knowledge Sharing Initiative 1993; Labrou and Finin1997a; Labrou and Finin 1997b), which relate agent communications to agent mental state (Sadek 1992). For instance, in FIPA ACL, each communicative act specification is equipped by a feasibility precondition (FP).that must hold for the sender, and a rational effect that the sender may suppose to occur on the receiver, even though such an effect is not actually mandatory for the receiver, so as to preserve its autonomy. Both these specification, as well as the actual message content, are given in terms of a quantified, multi-modal logic with modal operators for beliefs (B), desires (C), uncertain beliefs (U), and intentions (I), called Semantic Language (SL) (FIPA 2000), which has its root from the work on the BDI framework. Despite FIPA not mandating any actual architecture for agents, FIPA ACL Semantics perfectly assumes that the agent behavior can be interpreted in terms of a BDI-like architecture,1 which can be pictorially represented. The agent internal machinery should be clearly aware of any communicative act sent or received by the agent (Act). It should be noted that since rational effect are not obligatory for the agent, their logics are not conceptually part of the represented portion of the agent. Instead, details about rational e€ ects can be used by an agent internal machinery to assume the effect on the receiver of the acts it sends, whereas details about the feasibility preconditions can be used to infer the mental state of the sender.
Decoupling Specification from Implementation
Almost all the known semantics for ACLs are based on the concept of agent mental state, which may result in sending a communicative act, and how the reception of a communicative act may affect the receiver mental state or at least, which are the effects on the receiver that the sender may suppose to occur. In spite, these semantics do not mandate any specific architecture for agents, and are meant to be applicable in general fashion; they implicitly promote the concept of mental state as a notion in the specification of ACLs. This is likely to provide a good support for the cooperation of agents built over BDI frameworks. In fact, these specifications may drive the design of agent protocols (Bergenti, Botelho, Rimassa, and Somacher (2002), may help designing agent planners exploiting the notions of feasibility preconditions and rational effects to understand the effect to communications
(Bergenti and Poggi 2001), may provide support to the verification of conformance of an agent implementation with respect to a specification, even though, at this time, this problem has yet to be faced (Wooldridge 1998).On the other hand, serious limits in the workability and applicability become apparent when the ACL specification has to support cooperation among agents built over different architectures. In practice, in those cases where the agent wraps a physical resource, a legacy system, an information system, and so on, it is unclear what is the benefit of supposing its behavior can be understood. Viroli and Omicini (2001).For instance, it is unclear how do feasibility preconditions apply in these cases, and what is the benefit of supposing that some rational effect may occur. Also, this kind of specification is useless to the end of designing the agent wrapper, and makes the problem of proving conformance even more complex. As far as an ACL is concern to help standardizing agent cooperation, it is clear that the agent abstract architecture implicitly assumed by the ACL. Semantics should be as much abstract and implementation-decoupled as required in order to provide for a widely applicable specification tool. To this end, this easy consider the abstract architecture for agents derived from the ontology developed in Viroli, Moro, and Omicini (2001), which captures the very notion of observation in computer systems. By this framework, agents are represented as observable sources of information, providing their unique individual viewpoint over the world and making it available to other agents.
Here the roles of semantic in communication could be explained succinctly by examine the following sequences of communication conversation with the use of semantic set. There are certain number of magnitude of semantic in term of space in this case is four: the normative positions of the speaker and hearer before and after the utterance. Therefore, if d = 4, the number of possible communicative acts is 22352! (Computation of this figure may not be necessary in this context). Consistency is to be anticipated in a domain in which, assumption that agents can observe a common scene and ground their utterances in it, is simply irrational .The focus of that consistency needs to be squarely upon how communication can be described, rather than up library of communication primitives. The aim is to provide agents with a system by which they can tune a language with great accuracy to the needs at hand, and the ability to do this outweighs the potential pitfalls of any particular language. Construction process, such as Support Vector Machine (SVM) is thus well suited to domains in which agents might reasonably be expected not to suggest a huge number of different primitives. Primitives were to be submitted for consideration. This would bring down the complexity dramatically (it would no longer be necessary to work on the power set of the points in semantic space), but at the cost of requiringlonger sequences of primitives in from it is one of the advantages of the approach.
To explain the function of SVM, three agents could be considered, each of them wishes to introduce communicative acts such as commands, permissive, and co missive acts into a shared communication language. Each act specifies (or partially specifies) transitions of the speaker and hearer acts are represented Lindahl (1997).Here with a set of transitions for the speaker and an equivalent set for the hearer. For instance, an act may state that, before the act, the speaker, i is permitted to remain passive toward the propositional content of the act and after the act, i is committed to remain passive. In other words, i is, before the act, in any of the Lindahl states 1, 2, or 4 and after the act in the state 6. Thus, the set of transitions for the speaker is: {(1, 6), (2, 6), (4, 6)}. For the hearer j, before the act, j is permitted to bring about p and after the act, j is committed to bring about p. In other words, j is, before the act in any of the states 1, 2, or 3 and after the act in state 5. Thus, the set of transitions for the hearer is: {(1, 5), (2, 5), (3, 5)}. This particular communicative act results in the hearer being obliged to bring about p and the speaker being obliged to remain passive toward p: the hearer must bring about p and the speaker cannot interfere. The initial state of the semantic fixing between these three agents is that agents 1, 2, and3 are interested in the following sets of communicative acts being included in the language:
Agent 1. This agent wishes to introduce two actions into the language.
1. a, A command that commits the hearer to bring about p such that the hearer is not a priori forbidden from doing so.
Speaker: {}
Hearer: {(1, 5), (2, 5), (3, 5)}
1.b An act that commits the speaker to bring about p such that the agent is a priori forbidden from doing so.
Speaker: {(1, 5), (2, 5), (3, 5)}
Hearer: {}
Agent 2. This agent wishes to introduce two actions into the language.
2. a An act that permits the hearer to bring about p such that the agent is a priori committed to remain passive.
Speaker: {}
Hearer: {(6, 2)}
2.b An act that commits the hearer to remain passive toward p such that the agent is a priori permitted to doing so or remaining passive.
Speaker: {}
Hearer: {(2, 6)}
Agent 3. This agent wishes to introduce two actions into the language:
3. a, A command that commits the hearer to bring about p and the speaker cannot
Interfere.
Speaker: {(1, 6), (2, 6), (4, 6)}
Hearer: {(1, 5), (2, 5), (3, 5)}
3. b A put-option act.
Speaker: {(2, 6)}
Hearer: {(6, 2)}
SVM then proceeds in the following way:
Round 0. Agent 1 broadcasts initiate (1, 2, and 3) (1-2-3 is the casting vote sequence).
The language, L is initialized. Each communicative act specification refers to the changes in normative position of the agents that will take on the roles of speaker and hearer when the act is used during communication. This could be seen in this conversation between three agents
Round 1. Agent 1 has the casting vote. Agent 1 broadcasts suggestion (1.a); agent 2 broadcasts suggestion (2.a); and agent 3 broadcasts suggestion (3.a). There is a tie. However, rather than using its casting vote to compel the inclusion of 1.a, agent 1 decides to endorse agent 3’s suggestion. Agent 1 broadcasts suggestion (3.a), and so this act is included in L.
Round 2. Agent 2 has the casting vote. Agent 1 broadcasts suggestion (1.b); agent 2 broadcasts suggestion (2.a); and agent 3 broadcasts suggestion (3.b). There is a, tie, and so the agent with the casting vote, agent 2, broadcasts suggestion (2.a). 2.a is included in L.
Round 3. Agent 3 has the casting vote. Agent 1 broadcasts suggestion (1.b);agent 2 broadcasts suggestion(2.b); and agent 3 broadcasts suggestion(3.b). There is tie, Although 2.a and 2.b use the same transitions as 3.b, 3.b is being introduced for a different purpose-for the trading of options-and so agent 3 uses the casting vote to broadcast suggestion (3.b). 3.b is included in L.
Round 4. Agent 1 has the casting vote. Agent 1 broadcasts suggestion (1.b); agent 2 broadcasts suggestion (2.b); and agent 3 broadcasts suggestion (null). There is
a tie, and so agent 1 uses the casting vote and broadcasts suggestion(1.b). 1. b is
Included in L.
Round 5. Agent 2 has the casting vote. Agent 1 broadcasts suggestion (null); agent 2 broadcasts suggestion (2.b); and agent 3 broadcasts suggestion (null). 2.b has the only vote, and so this is included in L.
Round 6. Agent 3 has the casting vote. Agent 1 broadcasts suggestion (null); agent
2 broadcasts suggestion (null); and agent 3 broadcasts suggestion (null). SVM terminates.
L = {3.a, 2.a, 3.b, 1.b, 2.b}.
Here, suppose that agent 2 is responsible for access to an information source. The two
acts introduced by these agents, 2.a and 2.b, allow it to permit and forbid access. Although agent 3 is not in control of this information source.. Agent 2, the manager agent is interested in issuing commands and allowing agents to commit to activities, hence its interest in 1.a and 1.b. It does, however, accept the inclusion of 3.a rather than 1.a-it accepts that it should not interfere with agents to whom it has given commands. This simple example explains sorts of communicative actions that can be included in a common language and how the simple voting mechanism may be used to construct such a language. This language can be seen as a subset of a more complete language for managing the activities of agents within an organization. Indeed there all kinds of slight distinctions, but these distinctions have real operational value, which can be exploited by the agents themselves.
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Finally, the work of Steels and Kaplan (1999) tackles the problem of language acquisition by an axes .Thus focus on a specific semantic space, having axes of color and position. The individual primitives discussed have either specific values on one or more axes (“red,” “blue and on the edge”), or have ranges of values on one or more axes (“toward the center,” “close to the left and toward the top”). This easy advocated a new approach to agent communication languages. Rather than viewing the specification as an off-line, design-time process, it is clearer now that open multi-agent systems should be a dynamic, run-time process.. Thus, agents can use their knowledge of the dialog type, their communication objectives, and their social relationships with one another to tailor the communication language to their prevailing circumstances hence the role of semantic cannot be underrated in communication.
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