Dustin Dannenhauer

Parallax Advanced Research

Anticipatory thinking is necessary for managing risk in the safety‐ and mission‐critical domains where AI systems are being deployed. We analyze the intersection of anticipatory thinking, the optimization paradigm, and metaforesight to advance our understanding of AI systems and their adaptive capabilities when encountering low‐likelihood/high‐impa...

Anticipatory thinking drives our ability to manage risk - identification and mitigation - in everyday life, from bringing an umbrella when it might rain to buying car insurance. As AI systems become part of everyday life, they too have begun to manage risk. Autonomous vehicles log millions of miles, StarCraft and Go agents have similar capabilities...

Developing artificial intelligence approaches to overcome novel, unexpected circumstances is a difficult, unsolved problem. One challenge to advancing the state of the art in novelty accommodation is the availability of testing frameworks for evaluating performance against novel situations. Recent novelty generation approaches in domains such as Sc...

To be robust to surprising developments, an intelligent agent must be able to respond to many different types of unexpected change in the world. To date, there are no general frameworks for defining and characterizing the types of environment changes that are possible. We introduce a formal and theoretical framework for defining and categorizing en...

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 t...

Computational metacognition represents a cognitive systems perspective on high-order reasoning in integrated artificial systems that seeks to leverage ideas from human metacognition and from metareasoning approaches in artificial intelligence. The key characteristic is to declaratively represent and then monitor traces of cognitive activity in an i...

Goal-driven autonomy is an agent model for managing a dynamic environment by reasoning about current and potential goals while planning and acting. Since unexpected events and conditions may cause an agent’s goals and plans to become invalid or infeasible, an agent with goal-driven autonomy should monitor the environment against its expectations. D...

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...

Anticipatory thinking is a complex cognitive process for assessing and managing risk in many contexts. Humans use anticipatory thinking to identify potential future issues and proactively take actions to manage their risks. In this paper we define a cognitive systems approach to anticipatory thinking as a metacognitive goal reasoning mechanism. The...

Dungeon Crawl Stone Soup is a popular, single-player, free and open-source rogue-like video game with a sufficiently complex decision space that makes it an ideal testbed for research in cognitive systems and, more generally, artificial intelligence. This paper describes the properties of Dungeon Crawl Stone Soup that are conducive to evaluating ne...

Cognitive agents operating in complex and dynamic domains benefit from significant goal management. Operations on goals include formulation, selection, change, monitoring and delegation in addition to goal achievement. Here we model these operations as transformations on goals. An agent may observe events that affect the agent’s ability to achieve...

We present a metacognitive, integrated, dual-cycle architecture whose function is to provide agents with a greater capacity for acting robustly in a dynamic environment and managing unexpected events. We present MIDCA 1.3, an implementation of this architecture which explores a novel approach to goal generation, planning and execution given surpris...

Goal Driven Autonomy (GDA) is an agent model for reasoning about goals while acting in a dynamic environment. Since anomalous events may cause an agent's current goal to become invalid, GDA agents monitor the environment for such anomalies. When domains are both partially observable and dynamic, agents must reason about sensing and planning actions...

We present a metacognitive, integrated, dual-cycle architecture whose function is to provide agents with a greater capacity for acting robustly in a dynamic environment and managing unexpected events. We present MIDCA 1.3, an implementation of this architecture which explores a novel approach to goal generation, planning and execution given surpris...

We present LUiGi-H a goal-driven autonomy (GDA) agent. Like other GDA agents it introspectively reasons about its own expectations to formulate new goals. Unlike other GDA agents, LUiGi-H uses cases consisting of hierarchical plans and semantic annotations of the expectations of those plans. Expectations indicate conditions that must be true when p...

Goal-driven autonomy (GDA) agents reason about goals while introspectively examining if their course of action matches their expectations. Many GDA agents adopt a hierarchical planning model to generate plans but limit reasoning with expectations to individual actions or projecting the expected state. In this paper we present a relaxation of this l...

Metareasoning is an important capability for autonomous systems, particularly for those being deployed on long duration missions. An agent with increased self-observation and the ability to control itself in response to changing environments will be more capable in achieving its goals. This is essential for long-duration missions where system desig...

IBM's Watson uses a variety of scoring algorithms to rank candidate answers for natural language questions. These scoring algorithms played a crucial role in Watson's win against human champions in Jeopardy!. We show that this same technique can be implemented within a real-time strategy (RTS) game playing goal-driven autonomy (GDA) agent. Previous...

Starcraft, a commercial Real-Time Strategy (RTS) game that has enjoyed world-wide popularity (including televised professional matches), is a challenging domain for automated computer agents. Evidence of this difficulty comes not only from characteristics of the game (massive state space, stochastic actions, partial visibility, etc.) but also from...