Heuristic or Algorithmic Problem-Solving Approach

Algorithmic or Heuristic Problem-Solving Method This study explores the problem surrounding the learners of AGI architecture within reference to decision making capabilities for the real world operation. While the operation of the AGI architecture can be applicable within a real world environment, however, few applicable methodologies and theories support its functionalities. Due to the inherent time-constraint, a direct application to the decision-making theory is not applicable. This study presents the predictive heuristics theory to solve the problem.

Objective of this paper is to use the heuristic theoretical framework to solve the problem posed by the application of AGI system, which is inherent with the time constraints. Heuristic Application to solve the AGI System Application In the contemporary environment, human problems are in a large scale where the decision-making procedures are subject to time constraints. One of these problems is the application of AGI architecture where decision is time constraint.

The resources and decision theoretical framework fail to address this type of stochastic nature, thus, the predictive heuristic is a viable solution to the decision-making problems posed by the AGI architecture. The heuristics are defined as strategies of using readily accessible, loosely applicable, and information resources to solve human and machine problems. (Helgason, Thorisson, Nivel, et al. 2011). Fig 1: AGI System Search Problem The fig 1 reveals the typical AGI architecture problem that provides the tree structure where each level corresponds atomic moment of time.

Where a) represents moment of time, b) represent the T1 (next moment of time) and S0 enumerates possible future state from S1, 0 to S1, n. All the states are used to evaluate the heuristic utility functions. The d) reveals the comparison of the utility value and the results reveals the expansion evaluated state. Fig 2: Predictive Heuristics Approach to Solve the AGI Learning Problems The fig 2 reveals the predictive heuristics to solve the problem where a) is the fine grained or continuous axis of time.

The b) is set of suggested action, and c) is the most desirable highest expected value. Analysis of the problem reveals that predictive heuristic is able to solve the AGI architectural problem using the state-space search as well as decision theory. Thus, the heuristic has been able to bridge the gap caused by the traditional decision theory and time constraint within the real world environment.

While the heuristic approach may be able to address the problem caused by the time constraint, however, the Zeigarnik approach to learning may not be an appropriate method to solve the problem because Zeigarnik believes that people are able to remember interrupted or uncompleted tasks better than the completed tasks. Zeigarnik is not an appropriate approach to solve the problem demonstrated in the AGI system because Zeigarnik focuses on an unfulfilled goal. Conclusion In the contemporary environment, some problems can be solved using systematic instructions based on the algorithms technique.

For example, it is possible for a professional librarian to assemble a new bookcase following directions of assembly, which comes with a new package. By using the Pythagorean Theorem, it is possible to arrive at a correct solution by following the algorithms.