Christian J. Muise

Dsharp: fast d-DNNF compilation with sharpSAT

Knowledge compilation is a compelling technique for dealing with the intractability of propositional reasoning. One particularly effective target language is Deterministic Decomposable Negation Normal Form (d-DNNF). We exploit recent advances in #SAT solving in order to produce a new state-of-the-art CNF → d-DNNF compiler: Dsharp. Empirical results demonstrate that Dsharp is generally an order of magnitude faster than c2d, the de facto standard for compiling to d-DNNF, while yielding a representation of comparable size.

SAT-Based analysis and quantification of information flow in programs

Quantitative information flow analysis (QIF) is a portfolio of security techniques quantifying the flow of confidential information to public ports. In this paper, we advance the state of the art in QIF for imperative programs. We present both an abstract formulation of the analysis in terms of verification condition generation, logical projection and model counting, and an efficient concrete implementation targeting ANSI C programs. The implementation combines various novel and existing SAT-based tools for bounded model checking, #SAT solving in presence of projection, and SAT preprocessing. We evaluate the technique on synthetic and semi-realistic benchmarks.

Probabilistically Estimating Backbones and Variable Bias: Experimental Overview

Backbone variables have the same assignment in all solutions to a given constraint satisfaction problem; more generally, biasrepresents the proportion of solutions that assign a variable a particular value. Intuitively such constructs would seem important to efficient search, but their study to date has been from a mostly conceptual perspective, in terms of indicating problem hardness or motivating and interpreting heuristics. Here we summarize a two-phase project where we first measure the ability of both existing and novel probabilistic message-passing techniques to directly estimate bias and identify backbones for the Boolean Satisfiability (SAT) Problem. We confirm that methods like Belief Propagation and Survey Propagation---plus Expectation Maximization-based variants---do produce good estimates with distinctive properties. The second phase demonstrates the use of bias estimation within a modern SAT solver, exhibiting a correlation between accurate, stable, estimates and successful backtracking search. The same process also yields a family of search heuristics that can dramatically improve search efficiency for the hard random problems considered.

Monitoring the execution of partial-order plans via regression

Partial-order plans (POPs) have the capacity to compactly represent numerous distinct plan linearizations and as a consequence are inherently robust. We exploit this robustness to do effective execution monitoring. We characterize the conditions under which a POP remains viable as the regression of the goal through the structure of a POP. We then develop a method for POP execution monitoring via a structured policy, expressed as an ordered algebraic decision diagram. The policy encompasses both state evaluation and action selection, enabling an agent to seamlessly switch between POP linearizations to accommodate unexpected changes during execution. We demonstrate the effectiveness of our approach by comparing it empirically and analytically to a standard technique for execution monitoring of sequential plans. On standard benchmark planning domains, our approach is 2 to 17 times faster and up to 2.5 times more robust than comparable monitoring of a sequential plan. On POPs that have few ordering constraints among actions, our approach is significantly more robust, with the ability to continue executing in up to an exponential number of additional states.

Artificial Social Reasoning: Computational Mechanisms for Reasoning about Others

With a view to supporting expressive, but tractable, collaborative interactions between humans and agents, we propose an approach for representing heterogeneous agent models, i.e., with potentially diverse mental abilities and holding stereotypical characteristics as members of a social reference group. We build a computationally grounded mechanism for progressing their beliefs about others’ beliefs, supporting stereotypical as well as empathic reasoning. We comment on how this approach can be used to build finite-state games, restricting the analysis of possibly large-scale problems by focusing only on the set of plausible evolutions.

Towards team formation via automated planning

Cooperative problem solving involves four key phases: (1) finding potential members to form a team, (2) forming the team, (3) formulating a plan for the team, and (4) executing the plan. We extend recent work on multi-agent epistemic planning and apply it to the problem of team formation in a blocksworld scenario. We provide an encoding of the first three phases of team formation from the perspective of an initiator, and show how automated planning efficiently yields conditional plans that guarantee certain collective intentions will be achieved. The expressiveness of the epistemic planning formalism, which supports modelling with the nested beliefs of agents, opens the prospect of broad applicability to the operationalisation of collective intention.

Optimal Partial-Order Plan Relaxation via MaxSAT

Partial-order plans (POPs) are attractive because of their least-commitment nature, which provides enhanced plan flexibility at execution time relative to sequential plans. Current research on automated plan generation focuses on producing sequential plans, despite the appeal of POPs. In this paper we examine POP generation by relaxing or modifying the action orderings of a sequential plan to optimize for plan criteria that promote flexibility. Our approach relies on a novel partial weighted MaxSAT encoding of a sequential plan that supports the minimization of deordering or reordering of actions. Using a similar technique, we further demonstrate how to remove redundant actions from the plan, and how to combine this criterion with the objective of maximizing a POPs flexibility. Our partial weighted MaxSAT encoding allows us to compute a POP from a sequential plan effectively. We compare the efficiency of our approach to previous methods for POP generation via sequential-plan relaxation. Our results show that while an existing heuristic approach consistently produces the optimal deordering of a sequential plan, our approach has greater flexibility when we consider reordering the actions in the plan while also providing a guarantee of optimality. We also investigate and confirm the accuracy of the standard flex metric typically used to predict the true flexibility of a POP as measured by the number of linearizations it represents.

\#\exists SAT: Projected Model Counting

Model counting is the task of computing the number of assignments to variables \(\mathcal{V}\) that satisfy a given propositional theory F. The model counting problem is denoted as #SAT. Model counting is an essential tool in probabilistic reasoning. In this paper, we introduce the problem of model counting projected on a subset of original variables that we call priority variables \(\mathcal{P}\subseteq \mathcal{V}\). The task is to compute the number of assignments to \(\mathcal{P}\) such that there exists an extension to non-priority variables \(\mathcal{V}\setminus \mathcal{P}\) that satisfies F. We denote this as \(\#\exists \)SAT. Projected model counting arises when some parts of the model are irrelevant to the counts, in particular when we require additional variables to model the problem we are counting in SAT. We discuss three different approaches to \(\#\exists \)SAT (two of which are novel), and compare their performance on different benchmark problems.

LTL Realizability via Safety and Reachability Games

In this paper, we address the problem of LTL realizability and synthesis. State of the art techniques rely on so-called bounded synthesis methods, which reduce the problem to a safety game. Realizability is determined by solving synthesis in a dual game. We provide a unified view of duality, and introduce novel bounded realizability methods via reductions to reachability games. Further, we introduce algorithms, based on AI automated planning, to solve these safety and reachability games. This is the the first complete approach to LTL realizability and synthesis via automated planning. Experiments illustrate that reductions to reachability games are an alternative to reductions to safety games, and show that planning can be a competitive approach to LTL realizability and synthesis.

Variable-Delay Controllability

In temporal planning, agents must schedule a set of events satisfying a set of predetermined constraints. These scheduling problems become more difficult when the duration of certain actions are outside the agent’s control. Delay controllability is the generalized notion of whether a schedule can be constructed in the face of uncertainty if the agent eventually learns when events occur. Our work introduces the substantially more complex setting of determining variable-delay controllability, where an agent learns about events after some unknown but bounded amount of time has passed. We provide an efficient O(n) variable-delay controllability checker and show how to create an execution strategy for variable-delay controllability problems. To our knowledge, these essential capabilities are absent from existing controllability checking algorithms. We conclude by providing empirical evaluations of the quality of variable-delay controllability results as compared to approximations that use fixed delays to model the same problems.