In part motivated by topics such as agency safety, there is an increasing interest in goal reasoning, a form of agency where the agents formulate their own goals. One of the crucial aspects of goal reasoning agents is their ability to detect if the execution of their courses of actions meet their own expectations. We present a taxonomy of different forms of expectations as used by goal reasoning agents when monitoring their own execution. We summarize and contrast the current understanding of how to define and check expectations based on different knowledge sources used. We also identify gaps in our understanding of expectations.
Complex, real-world domains may not be fully modeled for an agent, especially if the agent has never operated in the domain before. The agent's ability to effectively plan and act in such a domain is influenced by its knowledge of when it can perform specific actions and the effects of those actions. We describe a novel exploratory planning agent that is capable of learning action preconditions and effects without expert traces or a given goal. The agent's architecture allows it to perform both exploratory actions as well as goal-directed actions, which opens up important considerations for how exploratory planning and goal planning should be controlled, as well as how the agent's behavior should be explained to any teammates it may have. The contributions of this work include a new representation for contexts called Lifted Linked Clauses, a novel exploration action selection approach using these clauses, an exploration planner that uses lifted linked clauses as goals in order to reach new states, and an empirical evaluation in a scenario from an explorationfocused video game demonstrating that lifted linked clauses improve exploration and action model learning against non-planning baseline agents.
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