Hierarchical Goal Networks: Formalisms and Algorithms for Planning and Acting

In real-world applications of AI and automation such as in robotics, computer game playing and web-services, agents need to make decisions in unstructured environments that are open-world, dynamic and partially observable. In the AI and Robotics research communities in particular, there is much interest in equipping robots to operate with minimal human intervention in diverse scenarios such as in manufacturing plants, homes, hospitals, etc. Enabling agents to operate in these environments requires advanced planning and acting capabilities, some of which are not well supported by the current state of the art automated planning formalisms and algorithms. To address this problem, in my thesis I propose a new planning formalism that addresses some of the inadequacies in current planning frameworks, and a suite of planning and acting algorithms that operate under this planning framework. The main contributions of this thesis are: - Hierarchical Goal Network (HGN) Planning Formalism. This planning formalism combines aspects (and therefore harnesses advantages) of Classical Planning and Hierarchical Task Network (HTN) Planning, two of the most prominent planning formalisms currently in use. In particular, HGN planning algorithms, while retaining the efficiency and scalability advantages of HTNs, also allows incorporation of heuristics and other reasoning techniques from Classical Planning. - Planning Algorithms. Goal Decomposition Planner (GDP) and the Goal Decomposition with Landmarks (GoDeL) planner are two HGN planning algorithms that combines hierarchical decomposition with classical planning heuristics to outperform state-of-the-art HTN planners like SHOP and SHOP2. - Integration with Robotics. The Combined HGN and Motion Planning (CHaMP) algorithm integrates GoDeL with low-level motion and manipulation planning algorithms in Robotics to generate plans directly executable by robots. Given the need for autonomous agents to operate in open, dynamic and unstructured environments and the obvious need for high-level deliberation capabilities to enable intelligent behavior, the planning-and-acting systems that are developed as part of this thesis may provide unique insights into ways to realize these systems in the real world

Extending classical planning with state constraints: Heuristics and search for optimal planning

We present a principled way of extending a classical AI planning formalism with systems of state constraints, which relate - sometimes determine - the values of variables in each state traversed by the plan. This extension occupies an attractive middle ground between expressivity and complexity. It enables modelling a new range of problems, as well as formulating more efficient models of classical planning problems. An example of the former is planning-based control of networked physical systems - power networks, for example - in which a local, discrete control action can have global effects on continuous quantities, such as altering flows across the entire network. At the same time, our extension remains decidable as long as the satisfiability of sets of state constraints is decidable, including in the presence of numeric state variables, and we demonstrate that effective techniques for cost-optimal planning known in the classical setting - in particular, relaxation-based admissible heuristics - can be adapted to the extended formalism. In this paper, we apply our approach to constraints in the form of linear or non-linear equations over numeric state variables, but the approach is independent of the type of state constraints, as long as there exists a procedure that decides their consistency. The planner and the constraint solver interact through a well-defined, narrow interface, in which the solver requires no specialisation to the planning contextThis work was supported by ARC project DP140104219, “Robust AI Planning for Hybrid Systems”, and in part by ARO grant W911NF1210471 and ONR grant N000141210430

Incorporating Domain-Independent Planning Heuristics in Hierarchical Planning

Heuristics serve as a powerful tool in modern domain-independent planning (DIP) systems by providing critical guidance during the search for high-quality solutions. However, they have not been broadly used with hierarchical planning techniques, which are more expressive and tend to scale better in complex domains by exploiting additional domain-specific knowledge. Complicating matters, we show that for Hierarchical Goal Network (HGN) planning, a goal-based hierarchical planning formalism that we focus on in this paper, any poly-time heuristic that is derived from a delete-relaxation DIP heuristic has to make some relaxation of the hierarchical semantics. To address this, we present a principled framework for incorporating DIP heuristics into HGN planning using a simple relaxation of the HGN semantics we call Hierarchy-Relaxation. This framework allows for computing heuristic estimates of HGN problems using any DIP heuristic in an admissibility-preserving manner. We demonstrate the feasibility of this approach by using the LMCut heuristic to guide an optimal HGN planner. Our empirical results with three benchmark domains demonstrate that simultaneously leveraging hierarchical knowledge and heuristic guidance substantially improves planning performance

Hierarchical goal network planning: Initial results

In applications of HTN planning, repeated problems have arisen from the lack of correspondence between HTN tasks and classical-planning goals. We describe these problems and provide a new Hierarchical Goal Network (HGN) planning formalism that overcomes them. HGN tasks have syntax and semantics analogous to classical planning problems, and this has several benefits: HGN methods can be significantly simpler to write than HTN methods, there is a clear criterion for whether the HGN methods are correct, and classical-planning heuristic functions can be adapted for use in HGN planning. We define the HGN formalism, illustrate how to prove correctness of HGN methods, provide a planning algorithm called GNP (Goal Network Planner), and present experimental results showing that GNP’s performance compares favorably to that of SHOP2. We provide a planning-graph heuristic for optional use in GNP, and give experimental results showing the kinds of situations in which it helps or hurts GNP’s performance.

On the Feasibility of Planning Graph Style Heuristics for HTN Planning

In classical planning, the polynomial-time computability of propositional delete-free planning (planning with only positive effects and preconditions) led to the highly successful Relaxed Graphplan heuristic. We present a hierarchy of new computational complexity results for different classes of propositional delete-free HTN planning, with two main results: We prove that finding a plan for the delete-relaxation of a propositional HTN problem is NP-complete: hence unless P=NP, there is no directly analogous GraphPlan heuristic for HTN planning. However, a further relaxation of HTN planning (delete-free HTN planning with task insertion) is polynomial-time computable. Thus, there may be a possibility of using this or other relaxations to develop search heuristics for HTN planning

HTN Problem Spaces: Structure, Algorithms, Termination

For HTN planning, we formally characterize and classify four kinds of problem spaces in which each node represents a planning problem or subproblem. Two of the problem spaces are searched by current HTN planning algorithms; the other two problem spaces are new.This enables us to provide:Sufficient (and in one case, necessary) conditions for finiteness of each kind of problem space. The conditions can be evaluated up-front to see if an HTN planning problem is finite.Loop-detection tests that can be used in HTN planners to ensure termination when the problem space is finite.A way to compute the correct value for an upper-bound parameter in an HTN-to-PDDL translation algorithm published in IJCAI-2009.Planning algorithms that utilize the two new problem spaces to guarantee termination on broader classes of planning problems than previous HTN planning algorithms

The GoDeL Planning System: A More Perfect Union of Domain-Independent and Hierarchical Planning

One drawback of Hierarchical Task Network (HTN) planning is the difficulty of providing complete domain knowledge, i.e., a complete and correct set of HTN methods for every task. To provide a principled way to overcome this difficulty, we define a simple formalism that extends classical planning to include problem decomposition using methods, and a planning algorithm based on this formalism. In our formalism, the methods specify ways to achieve goals (rather than tasks as in conventional HTN planning), and goals may be achieved even when no methods are available. Our planning algorithm, GoDeL (Goal Decomposition with Landmarks), is sound and complete irrespective of whether the domain knowledge (i.e., the set of methods given to the planner) is complete. By comparing GoDeL’s performance with varying amounts of domain knowledge across three benchmark planning domains, we show experimentally that (1) GoDeL works correctly with partial planning knowledge, (2) GoDeL’s performance improves as more planning knowledge is given, and (3) when given full domain knowledge, GoDeL matches the performance of a state-of-the-art hierarchical planner.