With the rise of large language models (LLMs), AI agents as autonomous decision-makers present significant opportunities and challenges for human-AI cooperation. While many studies have explored human cooperation with AI as tools, the role of LLM-augmented autonomous agents in competitive-cooperative interactions remains under-examined. This study investigates human cooperative behavior by engaging 30 participants who interacted with LLM agents exhibiting different characteristics (purported human, purported rule-based AI agent, and LLM agent) in repeated Prisoner's Dilemma games. Findings show significant differences in cooperative behavior based on the agents' purported characteristics and the interaction effect of participants' genders and purported characteristics. We also analyzed human response patterns, including game completion time, proactive favorable behavior, and acceptance of repair efforts. These insights offer a new perspective on human interactions with LLM agents in competitive cooperation contexts, such as virtual avatars or future physical entities. The study underscores the importance of understanding human biases toward AI agents and how observed behaviors can influence future human-AI cooperation dynamics.

Random walks have become a popular component of recent planning systems. The increased exploration is a valuable addition to more exploitative search methods such as Greedy Best First Search (GBFS). A number of successful planners which incorporate random walks have been built. The work presented here aims to exploit the experience gained from building those systems. It begins a systematic study of the design space and alternative choices for building such a system, and develops a new random walk planner from scratch, with careful experiments along the way. Four major insights are: 1. a high state evaluation frequency is usually superior to the endpoint-only evaluation used in earlier systems, 2. adjusting the restarting parameter according to the progress speed in the search space performs better than any fixed setting, 3. biasing the action selection towards preferred operators of only the current state is better than Monte Carlo Helpful Actions, which depend on the number of times an action has been a preferred operator in previous walks, and 4. even simple forms of random walk planning can compete with GBFS.