Ontology-based examinational students work retrieval
CompSysTech '08 Proceedings of the 9th International Conference on Computer Systems and Technologies and Workshop for PhD Students in Computing
| Hi-index | 0.00 |
This article presents a computer model of intelligent agents inhabiting a virtual world. The software model we developed, the agent parameters are stored in relational tables. The agents based on this architecture can be visualized graphically. The agent’s architecture includes the following components: needs, emotions, actions, self-knowledge, knowledge of places and events, rules, meta-rules and characteristics. Every component can influence over every another component. The agent’s characteristics are energy, work, adaptation, and inertia. The agent performs work to gather features and to evaluate and use them. Then, its potential energy will be equal to the work on collecting features from the world. Inertia is the inflexibility of the intelligent agents towards the alteration of their state. A behavior rule is each statement that has one or more antecedents and conclusion. When the agent acquires some new information, it is able to change its behavior rule considering its own “principles”. The meta-rule conceives as abstract “principle” or “consciousness” of the agent. It has attitude to concrete behavior rules in situations, which requires behavior choice or reconsideration. They are fewer than the behavior rules and require more time and knowledge. The rules and meta-rules for agent’s behavior are dynamically content of relational tables and the program interprets them as a data. Thus, any modification is only the table content change. The explorer always knows the agent’s state and the reasons for this state. Every place, action, state, event or rule is a function of its features. Its value is a sum of the values of the features divided by their count. Every feature or a rule antecedent is a function of its emotions, emotions values, values and weights of the needs, and inertia. Every feature interacts with all other features. Relations between the components are shown and the possibilities for their quantitative representation are suggested. Expressions for calculating various parameters of the model including basic agent need weights and features of the places, actions, states, generalized agent states are presented and summarized. A coefficient used for reordering the basic needs is introduced. For the purposes of the experiment, the simple scenario is suggested.