Applying rules and Introduction to Production System
Here in this case I have applied only one rule (among four i.e., moving right, left, up and down) which is closer to goal. In practical, all the rules are applied and selects the best outcome which is closer to the goal and continues the generation of new state till the goal state is achieved.
| 2 | 8 | 3 |
| 1 | 6 | 4 |
| 7 | 5 |
↓
| 2 | 8 | 3 |
| 1 | 4 | |
| 7 | 6 | 5 |
↓
| 2 | 3 | |
| 1 | 8 | 4 |
| 7 | 6 | 5 |
↓
| 2 | 3 | |
| 1 | 8 | 4 |
| 7 | 6 | 5 |
↓
| 1 | 2 | 3 |
| 8 | 4 | |
| 7 | 6 | 5 |
↓
| 1 | 2 | 3 |
| 8 | 4 | |
| 7 | 6 | 5 |
Here my goal position is achieved. Starting from a start position and which was part of a space of the problem state which we have seen is a factorial 9. We could go and look at different configurations on the way generating lot of them and arrived at the goal position ‘D’ from a start position ‘s’. This paradigm of working like this is problem solving as state space search.
Production Systems and AI:
A production system consists of
1. Database
2. Operations
3. Control components.
Here this database is different from data based systems. Here database refers to set of rules and facts. Operations and Control components are the building blocks for constructing lucid descriptions of AI systems. Several AI systems exhibit little or high rigid isolation among the computational components of data, operations and control.
Production system involves an isolation of these computational components and thus seem to capture the essence of operation of most AI systems. Selecting rules and monitoring of those sequences of rules constitute the control strategy for production systems.
1) Database 2) Operations 3) Control System