Modeling Expert Knowledge in a Heuristic-Based Gin Rummy Agent

Abstract

We developed a heuristic-based reflex agent, Tonic, for the EAAI 2021 Undergraduate Research Challenge, which tasks competitors to create an autonomous player to play the card game gin rummy. Tonic's heuristics originate in expert knowledge and inform decision making for the three actions comprising a turn: drawing a card, discarding a card, and deciding when to knock. However, because these strategies are based in human intuition, there is often a lack of specificity to directly model them as algorithms. We developed parameterized models describing that intuition based on factors such as the number of turns played and an estimation of the opponent hand. To hone their performance, we conducted both manual analysis and parameter optimization (grid search) using self-play and play against a simple baseline agent. These heuristic models enable Tonic to win against the baseline agent at least 68% of the time.

The Seven-Times-Pass Method: Beneficial to the Optimization of Computer-Aided Teaching System

“…本文旨在通过他们在各类型、各阶段学校教育多 年积累的教学和管理经验将七遍通教学方法与八人组 管理方法相结合，进一步探索：计算机辅助应用七遍 通方法的认知计算和系统设计 [1]。具体讨论包括以下 几个方面：首先是教管学用社会化系统工程实践的概 念，知识模块精加工原理 [2]，七遍通方法，进而，是 人际和人机之间的协同智能 [3]特征，最后，是七遍通 认知计算和系统及其对计算机辅助教学 [4]及移动设备 辅助教学软件工具所需建模的相关准备，尤其是专家 知识获取 [5,6]和表达及针对性重用的策略应用。结语 部分进一步总结和探讨七遍通和八人组的教学模式结 构对 AI 时代教育信息化 [7]的意义。 [8]…”