Abstract Agents operating in complex and dynamic domains may observe changes that affect the agent’s ability to achieve its goals. Goal transformations allow unachievable goals to be converted into similar achievable goals. Previous work has examined transformation of goals within the state- spaced planner PRODIGY. This paper examines goal transformation within the MIDCA architecture. We introduce goal transformation at the metacognitive level as well as goal transformation in a Hierarchical Task Network planner and discuss the costs and benefits of each approach. We evaluate goal transformations in MIDCA using a modified, resource limited version of the classical blocksworld planning domain, demonstrating the benefit of achieving higher scoring goals due to goal transformations. 1 Introduction Effective performance in highly dynamic environments requires the discharge of many classical-planning assumptions held in the artificial intelligence commmunity. For example, the closed world assumption is not a practical strategy. The world is under continual change, and planning is often a matter of adjusting to the world as new information is discovered, whether during planning or during execution. However, the adjustment that planners classically perform given exogenous events entails change with regard to the knowledge concerning the current state of the world and, in response, adaptation of the current plan. During execution of plans, outcomes may diverge from expectations, so plans are again adjusted accordingly (see as far back as [Tate, et al., 1990]). The contention of this paper, however, is that the adjustment of the goals is often required in addition to adjustment of the plans themselves. Recent work on goal reasoning [Aha, et al., 2013; Hawes, 2011] has started to examine how intelligent agents can reason about and generate their own goals instead of always depending upon a human user directly. Broadly construed, the topic concerns complex systems that self- manage their desired goal states [Vattam, et al., 2013]. In the decision-making process, goals are not simply given as input from a human, rather they constitute representations that the system itself formulates. Here we examine the idea that goals also represent malleable knowledge structures that an agent can adapt and change as the situation warrants; they are not static. When the world changes during planning or during execution (in the real world, a clear chronological line between the two is not always present), goals may become obsolete. For example, it makes little sense to pursue the goal of securing a town center if the battlefield has shifted to an adjacent location. At such a point, a robust planner must be able to alter the goal minimally to compensate; otherwise, a correct plan to secure the old location will not be useful at execution time. We view a goal transformation to be a movement in a goal space and show how such a procedure can be incorporated into various mechanisms of a cognitive architecture. The rest of this paper is organized as follows. Section 2 introduces the goal transformation formalism, and Section 3 describes the MIDCA cognitive architecture within which we have implemented such transformations. Section 4 discusses the differences in goal transformation mechanisms (i.e., at the planning level or the metacognitive level). Section 5 presents experiments and discusses the results. Related work is discussed in Section 6, and we conclude in Section 7. 2 Goal Transformations Early work by Cox and Veloso [1998] a r g u e that goals can exist in an abstraction hierarchy whereby some goals specify desired state predicates that are more general than others. The concept introduced in their work is that an important strategy for re-planning in dynamic environ- ments is to shift goals along this hierarchy and other goal spaces. Such movement is called a goal transformation. Goal Transformation and Goal Reasoning Michael T. Cox Wright State Research Institute, Beavercreek, OH michael.cox@wright.edu Dustin Dannenhauer Lehigh University, Bethlehem, PA dtd212@lehigh.edu