Multi-answer Constrained Optimal Querying: Maximum Information Gain Coding

TL;DR

This paper introduces the Maximum Information Gain Coding (MIGC) method for constrained multi-answer querying, generalizing previous binary schemes to D-ary cases, and demonstrates its superior performance over existing methods.

Contribution

It generalizes the greedy binary separation coding to D-ary cases and proposes MIGC for constrained multi-answer queries, with theoretical optimality analysis.

Findings

MIGC outperforms GBSC and Shannon Coding in bits per symbol.

Theoretical discussion confirms MIGC's optimality under certain conditions.

Application to practical scenarios shows improved efficiency.

Abstract

As the rapidly developments of artificial intelligence and machine learning, behavior tree design in multiagent system or AI game become more important. The behavior tree design problem is highly related to the source coding in information theory. "Twenty Questions" problem is a typical example for the behavior tree design, usually used to explain the source coding application in information theory and can be solved by Huffman coding. In some realistic scenarios, there are some constraints on the asked questions. However, for general question set, finding the minimum expected querying length is an open problem, belongs to NP-hard. Recently, a new coding scheme has been proposed to provide a near optimal solution for binary cases with some constraints, named greedy binary separation coding (GBSC). In this work, we shall generalize it to D-ary cases and propose maximum information gain coding (MIGC) approach to solve the multi-answer decision constrained querying problem. The optimality of the proposed MIGC is discussed in theory. Later on, we also apply MIGC to discuss three practical scenarios and showcase that MIGC has better performance than GBSC and Shannon Coding in terms of bits persymbol.