In this paper we present an interface between a symbolic planner and a geometric task planner, which is different to a standard trajectory planner in that the former is able to perform geometric reasoning on abstract entities---tasks. We believe that this approach facilitates a more principled interface to symbolic planning, while also leaving more room for the geometric planner to make independent decisions. We show how the two planners could be interfaced, and how their planning and backtracking could be interleaved. We also provide insights for a methodology for using the combined system, and experimental results to use as a benchmark with future extensions to both the combined system, as well as to the geometric task planner.

Planning is the model-based approach to autonomous behavior where the agent behavior is derived automatically from a model of the actions, sensors, and goals. The main challenges in planning are computational as all models, whether featuring uncertainty and feedback or not, are intractable in the worst case when represented in compact form. In this book, we look at a variety of models used in AI planning, and at the methods that have been developed for solving them. The goal is to provide a modern and coherent view of planning that is precise, concise, and mostly self-contained, without being shallow. For this, we make no attempt at covering the whole variety of planning approaches, ideas, and applications, and focus on the essentials.

Wilson et al. propose a measure of flexibility in project scheduling problems and propose several ways of distributing flexibility over tasks without overrunning the deadline. These schedules prove quite robust: delays of some tasks do not necessarily lead to delays of subsequent tasks. The number of tasks that finish late depends, among others, on the way of distributing flexibility. In this paper I study the different flexibility distributions proposed by Wilson et al. and the differences in number of violations (tasks that finish too late). I show one factor in the instances that causes differences in the number of violations, as well as two properties of the flexibility distribution that cause them to behave differently. Based on these findings, I propose three new flexibility distributions. Depending on the nature of the delays, these new flexibility distributions perform as good as or better than the distributions by Wilson et al.

We formalize a simple but natural subclass of service domains for relational planning problems with object-centered, independent exogenous events and additive rewards capturing, for example, problems in inventory control. Focusing on this subclass, we present a new symbolic planning algorithm which is the first algorithm that has explicit performance guarantees for relational MDPs with exogenous events. In particular, under some technical conditions, our planning algorithm provides a monotonic lower bound on the optimal value function. To support this algorithm we present novel evaluation and reduction techniques for generalized first order decision diagrams, a knowledge representation for real-valued functions over relational world states. Our planning algorithm uses a set of focus states, which serves as a training set, to simplify and approximate the symbolic solution, and can thus be seen to perform learning for planning. A preliminary experimental evaluation demonstrates the validity of our approach.

In this article we introduce the Arcade Learning Environment (ALE): both a challenge problem and a platform and methodology for evaluating the development of general, domain-independent AI technology. ALE provides an interface to hundreds of Atari 2600 game environments, each one different, interesting, and designed to be a challenge for human players. ALE presents significant research challenges for reinforcement learning, model learning, model-based planning, imitation learning, transfer learning, and intrinsic motivation. Most importantly, it provides a rigorous testbed for evaluating and comparing approaches to these problems. We illustrate the promise of ALE by developing and benchmarking domain-independent agents designed using well-established AI techniques for both reinforcement learning and planning. In doing so, we also propose an evaluation methodology made possible by ALE, reporting empirical results on over 55 different games. All of the software, including the benchmark agents, is publicly available.

In this article we introduce the Arcade Learning Environment (ALE): both a challenge problem and a platform and methodology for evaluating the development of general, domain-independent AI technology. ALE provides an interface to hundreds of Atari 2600 game environments, each one different, interesting, and designed to be a challenge for human players. ALE presents significant research challenges for reinforcement learning, model learning, model-based planning, imitation learning, transfer learning, and intrinsic motivation. Most importantly, it provides a rigorous testbed for evaluating and comparing approaches to these problems. We illustrate the promise of ALE by developing and benchmarking domain-independent agents designed using well-established AI techniques for both reinforcement learning and planning. In doing so, we also propose an evaluation methodology made possible by ALE, reporting empirical results on over 55 different games. All of the software, including the benchmark agents, is publicly available.

We propose an approximation of the Possible Worlds Semantics (PWS) for action planning. A corresponding planning system is implemented by a transformation of the action specification to an Answer-Set Program. A novelty is support for postdiction wrt. (a) the plan existence problem in our framework can be solved in NP, as compared to $\Sigma_2^P$ for non-approximated PWS of Baral(2000); and (b) the planner generates optimal plans wrt. a minimal number of actions in $\Delta_2^P$. We demo the planning system with standard problems, and illustrate its integration in a larger software framework for robot control in a smart home.

She has (which includes a human user), while planning determines equipped the apartment with a series of service robots, the concrete actions that should be carried out in order to sensors and actuators which help her manage some of best support the perceived context. The domain description the physical and cognitive difficulties she has due to formalism used by SAM is based on metric temporal constraints; her age. Her home alerts her if she appears to be overcooking such domains model both the criteria for context inference her meals, and autonomously organizes when and the planning operators used for plan synthesis. The of the user and to contextually synthesize action plans for home recognizes when Malin is sleeping, eating and actuators in the intelligent environment. The knowledge representation scheme used in SAM is based State of the art robotic and sensor systems can be leveraged on Allen's Interval Relations (Allen 1984), augmented with to achieve intelligent functionalities that are useful in a number temporal bounds.

She has (which includes a human user), while planning determines equipped the apartment with a series of service robots, the concrete actions that should be carried out in order to sensors and actuators which help her manage some of best support the perceived context. The domain description the physical and cognitive difficulties she has due to formalism used by SAM is based on metric temporal constraints; her age. Her home alerts her if she appears to be overcooking such domains model both the criteria for context inference her meals, and autonomously organizes when and the planning operators used for plan synthesis. The of the user and to contextually synthesize action plans for home recognizes when Malin is sleeping, eating and actuators in the intelligent environment. The knowledge representation scheme used in SAM is based State of the art robotic and sensor systems can be leveraged on Allen's Interval Relations (Allen 1984), augmented with to achieve intelligent functionalities that are useful in a number temporal bounds.

She has (which includes a human user), while planning determines equipped the apartment with a series of service robots, the concrete actions that should be carried out in order to sensors and actuators which help her manage some of best support the perceived context. The domain description the physical and cognitive difficulties she has due to formalism used by SAM is based on metric temporal constraints; her age. Her home alerts her if she appears to be overcooking such domains model both the criteria for context inference her meals, and autonomously organizes when and the planning operators used for plan synthesis. The of the user and to contextually synthesize action plans for home recognizes when Malin is sleeping, eating and actuators in the intelligent environment. The knowledge representation scheme used in SAM is based State of the art robotic and sensor systems can be leveraged on Allen's Interval Relations (Allen 1984), augmented with to achieve intelligent functionalities that are useful in a number temporal bounds.