Common Sense ontology

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                                 Term paper

on        

        Common sense ontology

SUBMITTED TO:-

Lect. Rajeev kanday

                                                                                                            SUBMITTED BY:-

                                                                                Sahil Lalotra

                                                                                 REG No -11001398

Index

Introduction :

Artificial intelligence is a branch of computer research that attempts to understand human intelligence by building computer programs that simulate intelligent behaviors. It is concerned with the concepts and methods of symbolic inference, computer reasoning, and how the knowledge used to make those inferences is represented inside the machine (R.S. Engelmore, 1993).

According to John McCarthy (1980), who first recognized the overlap between ontology and the knowledge bases of logical AI systems, logic-based intelligent systems must first “list everything that exists, building an ontology of our world”. Since McCarthy made this observation, ontology has become intertwined with the development of artificial intelligence and of information systems science. Knowledge bases containing ontology support logics AI applications by providing the power of simulating knowledge for those automated reasoning mechanisms.

 INTELLIGENT AGENT

The definition of “agent” presented here is adapted from M. Wooldridge and N. R. Jennings (1995): “[A]n agent is a computer system that is situated in some environment, and that is capable of autonomous action in this environment in order to meet its design objectives.” This definition indicates that agents are able to act without the intervention of humans or other systems. They have control both over their own internal states and over their behaviors. Additionally, Figure 1 which gives an abstract, top-level view of an agent, indicates that an agent works within its environment. “The agent takes sensory input from the environment, and produces as output actions that affect it…. The interaction is usually an ongoing, non-terminating one” (M. Wooldridge, 2002).

The following are other qualities and abilities of intelligent agents:

􀁺 Reactivity: In order to fulfill design objectives, intelligent agents are able to perceive their environment and respond rapidly to changes that occur in it.

􀁺 Pro-activity: In order to fulfill design objectives, intelligent agents are able to exhibit goal-oriented behavior by taking initiative.

􀁺 Social ability: In order to fulfill design objectives, intelligent agents are able to interact with other agents and possibly humans.

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In many environments, some domains are too complex for an agent to handle on its own. Therefore, several agents working together to pursue their common goal may be a possible solution. The requirements for cooperation of a group of agents have been put forward

MULTI-AGENT SYSTEMS (MAS)

As mentioned above, intelligent agents operate and exist under certain environments. These environments might be open or closed, and they might or might not contain other agents. Although there are situations where an agent can operate feasibly by itself, the increasing interconnection and networking of computers make such situation rare, and in natural design purposes of agent systems the agent interacts with other agents. Thus, the idea of societies of agents has emerged, which we call multi-agent systems (MAS).

A multi-agent system is defined as “a loosely coupled network of autonomous agents that work together to solve problems that are beyond individual capabilities or knowledge” (E. Vicente, S. Dorgham, and Radu, 2005). Once the problems have been clearly defined, the agents can interact and collaborate with each other in order to accomplish the goals defined (Sycara K. P., 1998). Their flexibility is high enough that they are able to work under both homogeneous and heterogeneous environments.

Many multi-agent systems development tools are available on the market – for  example, SAGE, OSLO-Software, AgentScape, Aglets, JADE, and so forth. The newest one is JADE (Java Agent Development Framework), a Java-based, FIPA-compliant, multi-agent platform. Most developers have incorporated Jess into the MAS. Jess is a rule engine that is written in Java and is compatible with JADE.

Recently, there has been increasing research interest in applying the MAS approach in dealing with complex problems with a vast amount of data manipulation (S. Gao and H. Wang, 2005). Most multi-agent systems are designed to manipulate domain knowledge. However, coverage of domain-specific knowledge is no longer enough to support MAS to generate the best solution to complex problems.

ONTOLOGY AND INTELLIGENT AGENTS

As mentioned previously, ontology is a specification of the objects, concepts, and relationships in a particular area of a domain. Since agents are constructed by people, the agent’s creator must use a specific ontology to represent the agent’s knowledge. The agent must then represent its knowledge in the vocabulary of the specified ontology. All agents in one group that share the same ontology for knowledge representation have an understanding of the “words” in the agent communication language. Ontology editors are typically frame-based knowledge-representation systems that allow users to define an ontology and its components: classes, instances, relationships, and functions. Ontology editors offer a variety of features, such as the ability to translate an ontology into several representation languages or the ability for distributed groups to develop an ontology jointly over the Internet.