Bonet, Blai


Features, Projections, and Representation Change for Generalized Planning

arXiv.org Artificial Intelligence

Generalized planning is concerned with the characterization and computation of plans that solve many instances at once. In the standard formulation, a generalized plan is a mapping from feature or observation histories into actions, assuming that the instances share a common pool of features and actions. This assumption, however, excludes the standard relational planning domains where actions and objects change across instances. In this work, we extend the standard formulation of generalized planning to such domains. This is achieved by projecting the actions over the features, resulting in a common set of abstract actions which can be tested for soundness and completeness, and which can be used for generating general policies such as "if the gripper is empty, pick the clear block above x and place it on the table" that achieve the goal clear(x) in any Blocksworld instance. In this policy, "pick the clear block above x" is an abstract action that may represent the action Unstack(a, b) in one situation and the action Unstack(b, c) in another. Transformations are also introduced for computing such policies by means of fully observable non-deterministic (FOND) planners. The value of generalized representations for learning general policies is also discussed.


Planning With Pixels in (Almost) Real Time

AAAI Conferences

Recently, width-based planning methods have been shown to yield state-of-the-art results in the Atari 2600 video games. For this, the states were associated with the (RAM) memory states of the simulator. In this work, we consider the same planning problem but using the screen instead. By using the same visual inputs, the planning results can be compared with those of humans and learning methods. We show that the planning approach, out of the box and without training, results in scores that compare well with those obtained by humans and learning methods, and moreover, by developing an episodic, rollout version of the IW(k) algorithm, we show that such scores can be obtained in almost real time.


Completeness of Online Planners for Partially Observable Deterministic Tasks

AAAI Conferences

Partially observable planning is one of the most general and useful models for dealing with complex problems. In recent years there have been significant progress on the development of planners for deterministic models that offer strong theoretical guarantees over certain subclasses of tasks. These guarantees however are difficult to establish as they often involve reasoning about features that are specific to the planner and subclass of tasks. In this paper we develop a formal framework for reasoning about online planning over deterministic tasks, identify a set of general conditions that are sufficient to guarantee completeness, and obtain novel and simple planners that are complete over non-trivial and interesting classes of tasks. Building on top state-of-the-art online planners, we implement some of our ideas and make a comparison with a state-of-the-art online planner.


Abstraction Heuristics, Cost Partitioning and Network Flows

AAAI Conferences

Cost partitioning is a well-known technique to make admissible heuristics for classical planning additive. The optimal cost partitioning of explicit-state abstraction heuristics can be computed in polynomial time with a linear program, but the size of the model is often prohibitive. We study this model from a dual perspective and develop several simplification rules to reduce its size. We use these rules to answer open questions about extensions of the state equation heuristic and their relation to cost partitioning.


Higher-Dimensional Potential Heuristics for Optimal Classical Planning

AAAI Conferences

Potential heuristics for state-space search are defined as weighted sums over simple state features. Atomic features consider the value of a single state variable in a factored state representation, while binary features consider joint assignments to two state variables. Previous work showed that the set of all admissible and consistent potential heuristics using atomic features can be characterized by a compact set of linear constraints. We generalize this result to binary features and prove a hardness result for features of higher dimension. Furthermore, we prove a tractability result based on the treewidth of a new graphical structure we call the context-dependency graph . Finally, we study the relationship of potential heuristics to transition cost partitioning . Experimental results show that binary potential heuristics are significantly more informative than the previously considered atomic ones.


A Summary of the Twenty-Ninth AAAI Conference on Artificial Intelligence

AI Magazine

The Twenty-Ninth AAAI Conference on Artificial Intelligence, (AAAI-15) was held in January 2015 in Austin, Texas (USA) The conference program was cochaired by Sven Koenig and Blai Bonet. This report contains reflective summaries of the main conference, the robotics program, the AI and robotics workshop, the virtual agent exhibition, the what's hot track, the competition panel, the senior member track, student and outreach activities, the student abstract and poster program, the doctoral consortium, the women's mentoring event, and the demonstrations program.


A Summary of the Twenty-Ninth AAAI Conference on Artificial Intelligence

AI Magazine

The Twenty-Ninth AAAI Conference on Artificial Intelligence, (AAAI-15) was held in January 2015 in Austin, Texas (USA) The conference program was cochaired by Sven Koenig and Blai Bonet. This report contains reflective summaries of the main conference, the robotics program, the AI and robotics workshop, the virtual agent exhibition, the what's hot track, the competition panel, the senior member track, student and outreach activities, the student abstract and poster program, the doctoral consortium, the women's mentoring event, and the demonstrations program.


Policies that Generalize: Solving Many Planning Problems with the Same Policy

AAAI Conferences

We establish conditions under which memoryless policies and finite-state controllers that solve one partially observable non-deterministic problem (PONDP) generalize to other problems; namely, problems that have a similar structure and share the same action and observation space. This is relevant to generalized planning where plans that work for many problems are sought, and to transfer learning where knowledge gained in the solution of one problem is to be used on related problems. We use a logical setting where uncertainty is represented by sets of states and the goal is to be achieved with certainty. While this gives us crisp notions of solution policies and generalization, the account also applies to probabilistic  PONDs, i.e., Goal POMDPs.


LP-Based Heuristics for Cost-Optimal Planning

AAAI Conferences

Many heuristics for cost-optimal planning are based on linear programming. We cover several interesting heuristics of this type by a common framework that fixes the objective function of the linear program. Within the framework, constraints from different heuristics can be combined in one heuristic estimate which dominates the maximum of the component heuristics. Different heuristics of the framework can be compared on the basis of their constraints. With this new method of analysis, we show dominance of the recent LP-based state-equation heuristic over optimal cost partitioning on single-variable abstractions.  We also show that the previously suggested extension of the state-equation heuristic to exploit safe variables cannot lead to an improved heuristic estimate. We experimentally evaluate the potential of the proposed framework on an extensive suite of benchmark tasks.


Flow-Based Heuristics for Optimal Planning: Landmarks and Merges

AAAI Conferences

We describe a flow-based heuristic for optimal planning that exploits landmarks and merges. The heuristic solves a lin- ear programming (LP) problem that represents variables in SAS+ planning as a set of interacting network flow prob- lems. The solution to the LP provides a reasonable admissible heuristic for optimal planning, but we improve it considerably by adding constraints derived from action landmarks and from variable merges. Merged variables, however, can quickly grow in size and as a result introduce many new variables and constraints into the LP. In order to control the size of the LP we introduce the concept of dynamic merging that allows us to partially and incrementally merge variables, thus avoiding the formation of cross products of domains as done when merging variables in the traditional way. The two types of improvements (action landmarks and variable merges) to the LP formulation are orthogonal and general. We measure the impact on performance for optimal planning of each improvement in isolation, and also when combined, for a sim- ple merge strategy. The results show that the new heuristic is competitive with the current state of the art.