Hybrid planning with nonlinear continuous change is a significant challenge for existing planners. Prior works limit their scope to linear change or base their formalisms in model checking frameworkswith inherent limitations. We address nonlinear PDDL+ planning with anew encoding in first order logic over real valued functions. Our planner, PluReal, translates PDDL+ to this logical encoding and applies the dReal Satisfiability Modulo Theories (SMT) solver to construct plans. Unlike prior work that uses dReal in the hybrid system model checking tradition, PluReal is based in the planning as satisfiability (SAT) heritage. Adopting the SAT approach helps lift several unnatural restrictions that are imposed by the translation through hybrid systems and leads to improved scalability even without SMT solver variable selection heuristics.

This paper describes a novel combination of Java program analysis and automated learning and planning architecture to the domain of Java vulnerability analysis. The key feature of our "HACKAR:Helpful Advice for Code Knowledge and Attack Resilience'' system is its ability to analyze Java programs at development-time, identifying vulnerabilities and ways to avoid them. HACKAR uses an improved version of NASA's Java PathFinder (JPF) to execute Java programs and identify vulnerabilities. The system features new Hierarchical Task Network (HTN) learning algorithms that (1) advance state-of-the-art HTN learners with reasoning about numeric constraints, failures, and more general cases of recursion, and (2) contribute to problem-solving by learning a hierarchical dataflow representation of the program from the inputs of the program. Empirical evaluation demonstrates that HACKAR was able to suggest fixes for all of our test program suites. It also shows that HACKAR can analyze programs with string inputs that original JPF implementation cannot.

PDDL+ planning involves reasoning about mixed discrete-continuous change over time. Nearly all PDDL+ planners assume that continuous change is linear. We present a new technique that accommodates nonlinear change by encoding problems as nonlinear hybrid systems. Using this encoding, we apply a Satisfiability Modulo Theories (SMT) solver to find plans. We show that it is important to use novel planning- specific heuristics for variable and value selection for SMT solving, which is inspired by recent advances in planning as SAT. We show the promising performance of the resulting solver on challenging nonlinear problems.

Prior approaches to generating diverse plans in domain-independent  planning seek out variations on plan structure such as actions or  causal links used, or states entered.  Measuring such syntactic  differences between plans can be misleading because syntactically  different plans can be semantically identical.  We develop a  landmark-based plan distance measure that captures semantic  differences between plans. The landmark-based distance measure focuses on the disjunctive landmarks  satisfied by each plan.  We develop a  simple algorithm for finding diverse plans that is based upon the LAMA planner.  We illustrate that, in comparison with plan distance  measures,  landmark-based plan distance is  not as susceptible to including irrelevant or redundant actions in  plans to increase plan distance. Through extensive empirical evaluation, we find that  high landmark distance between plans implies high action set  distance, but not vice versa.  Landmark-based plan distance overcomes some of the weaknesses of syntactic plan distance measures and can be used to find plan sets that are both landmark diverse and action set diverse.

Prior approaches to generating diverse plans in domain-independent planning seek out variations on plan structure such as actions or causal links used, or states entered. As a result, these approaches can achieve great plan set diversity by synthesizing unnecessarily long plans. This type of misleading diversity may be useful if, for example, the plans are used as training data for a learner; however, they have arguably low value to a human decision maker. We present an approach to domain-independent diverse planning that systematically varies the semantic attributes of plans so that we do not arbitrarily inflate plan length to achieve diversity. Our contribution is based upon the fact that landmarks, which represent the minimally necessary subgoals (semantics) of a planning domain, can be disjunctive and hence satisfied in a number of ways. Varying the disjuncts that must be satisfied by alternative plans leads to a form of diversity that does not encourage irrelevant plan structure. We present an extension of the LAMA planner called DLAMA that generates multiple plans, each required to systematically satisfy alternative landmark disjuncts. We show that, in comparison with prior diverse planners, DLAMA reduces average plan length while achieving plan set diversity.

Recent work on planning in incomplete domains focuses on constructing plans that succeed despite incomplete knowledge of action preconditions and effects. As planning models become more expressive, such as in temporal planning, the types of incompleteness may not only change, but plans become more challenging to evaluate. The primary difficulty to temporal plan evaluation is accounting for temporal constraints that may not be satisfied under all interpretations of the incomplete domain. In this work, we formulate incomplete temporal plan evaluation as a generalization of the temporal consistency problem, called partial temporal consistency. We present a knowledge compilation approach that is combined with symbolic constraint propagation and model counting algorithms for counting the number of incomplete domain model interpretations under which a plan is consistent. We present an evaluation that identifies the aspects of incomplete temporal plans most impact performance.

Engineering complete planning domain descriptions is often very costly because of human error or lack of domain knowl- edge. Learning complete domain descriptions is also very challenging because many features are irrelevant to achieving the goals and data may be scarce. We present a planner and agent that respectively plan and act in incomplete domains by i) synthesizing plans to avoid execution failure due to ignorance of the domain model, and ii) passively learning about the domain model during execution to improve later re-planning attempts. Our planner DeFault is the first to reason about a domain’s incompleteness to avoid potential plan failure. DeFault computes failure explanations for each action and state in the plan and counts the number of interpretations of the incomplete domain where failure will occur. We show that DeFault performs best by counting prime implicants (failure diagnoses) rather than propositional models. Our agent Goalie learns about the preconditions and effects of incompletely-specified actions while monitoring its state and, in conjunction with DeFault plan failure explanations, can diagnose past and future action failures. We show that by reasoning about incompleteness (as opposed to ignoring it) Goalie fails and re-plans less and executes fewer actions.

A large part of the credit for this can be attributed squarely to the invention and deployment of powerful reachability heuristics. Most, if not all, modern reachability heuristics are based on a remarkably extensible data structure called the planning graph, which made its debut as a bit player in the success of GraphPlan, but quickly grew in prominence to occupy the center stage. Planning graphs are a cheap means to obtain informative look-ahead heuristics for search and have become ubiquitous in state-of-the-art heuristic search planners. We present the foundations of planning graph heuristics in classical planning and explain how their flexibility lets them adapt to more expressive scenarios that consider action costs, goal utility, numeric resources, time, and uncertainty.

The primary revolution in automated planning in the last decade has been the very impressive scale-up in planner performance. A large part of the credit for this can be attributed squarely to the invention and deployment of powerful reachability heuristics. Most, if not all, modern reachability heuristics are based on a remarkably extensible data structure called the planning graph, which made its debut as a bit player in the success of GraphPlan, but quickly grew in prominence to occupy the center stage. Planning graphs are a cheap means to obtain informative look-ahead heuristics for search and have become ubiquitous in state-of-the-art heuristic search planners. We present the foundations of planning graph heuristics in classical planning and explain how their flexibility lets them adapt to more expressive scenarios that consider action costs, goal utility, numeric resources, time, and uncertainty.