Dror Fried

SAT Conference 2024 Conference Paper

Entailing Generalization Boosts Enumeration

• Dror Fried
• Alexander Nadel
• Roberto Sebastiani
• Yogev Shalmon

Given a combinational circuit Γ with a single output o, AllSAT-CT is the problem of enumerating all solutions of Γ. Recently, we introduced several state-of-the-art AllSAT-CT algorithms based on satisfying generalization, which generalizes a given total Boolean solution to a smaller ternary solution that still satisfies the circuit. We implemented them in our open-source tool HALL. In this work we draw upon recent theoretical works suggesting that utilizing generalization algorithms, which can produce solutions that entail the circuit without satisfying it, may enhance enumeration. After considering the theory and adapting it to our needs, we enrich HALL’s AllSAT-CT algorithms by incorporating several newly implemented generalization schemes and additional SAT solvers. By conducting extensive experiments we show that entailing generalization substantially boosts HALL’s performance and quality (where quality corresponds to the number of reported generalized solutions per instance), with the best results achieved by combining satisfying and entailing generalization.

AAAI Conference 2024 Conference Paper

Mimicking Behaviors in Separated Domains (Abstract Reprint)

• Giuseppe De Giacomo
• Dror Fried
• Fabio Patrizi
• Shufang Zhu

Devising a strategy to make a system mimic behaviors from another system is a problem that naturally arises in many areas of Computer Science. In this work, we interpret this problem in the context of intelligent agents, from the perspective of LTLf, a formalism commonly used in AI for expressing finite-trace properties. Our model consists of two separated dynamic domains, D_A and D_B, and an LTLf specification that formalizes the notion of mimicking by mapping properties on behaviors (traces) of D_A into properties on behaviors of D_B. The goal is to synthesize a strategy that step-by-step maps every behavior of D_A into a behavior of D_B so that the specification is met. We consider several forms of mapping specifications, ranging from simple ones to full LTLf, and for each, we study synthesis algorithms and computational properties.

SAT Conference 2023 Conference Paper

AllSAT for Combinational Circuits

• Dror Fried
• Alexander Nadel
• Yogev Shalmon

Motivated by the need to improve the scalability of Intel’s in-house Static Timing Analysis (STA) tool, we consider the problem of enumerating all the solutions of a single-output combinational Boolean circuit, called AllSAT-CT. While AllSAT-CT is immediately reducible to enumerating the solutions of a Boolean formula in Conjunctive Normal Form (AllSAT-CNF), our experiments had shown that such a reduction, followed by applying state-of-the-art AllSAT-CNF tools, does not scale well on neither our industrial AllSAT-CT instances nor generic circuits, both when the user requires the solutions to be disjoint or when they can be non-disjoint. We focused on understanding the reasons for this phenomenon for the well-known iterative blocking family of AllSAT-CNF algorithms. We realized that existing blocking AllSAT-CNF algorithms fail to generalize efficiently for AllSAT-CT, since they are restricted to Boolean logic. Consequently, we introduce three dedicated AllSAT-CT algorithms that are ternary-logic-aware: a ternary simulation-based algorithm TALE, a dual-rail&MaxSAT-based algorithm MARS, and their combination. Specifically, we introduce in MARS two novel blocking clause generation approaches for the disjoint and non-disjoint cases. We implemented our algorithms in our new tool HALL. We show that HALL scales substantially better than any reduction to existing AllSAT-CNF tools on our industrial STA instances as well as on publicly available families of combinational circuits for both the disjoint and the non-disjoint cases.

JAIR Journal 2023 Journal Article

Mimicking Behaviors in Separated Domains

• Giuseppe De Giacomo
• Dror Fried
• Fabio Patrizi
• Shufang Zhu

Devising a strategy to make a system mimic behaviors from another system is a problem that naturally arises in many areas of Computer Science. In this work, we interpret this problem in the context of intelligent agents, from the perspective of ltlf, a formalism commonly used in AI for expressing finite-trace properties. Our model consists of two separated dynamic domains, DA and DB, and an LTLf specification that formalizes the notion of mimicking by mapping properties on behaviors (traces) of DA into properties on behaviors of DB. The goal is to synthesize a strategy that step-by-step maps every behavior of DA into a behavior of DB so that the specification is met. We consider several forms of mapping specifications, ranging from simple ones to full LTLf, and for each, we study synthesis algorithms and computational properties.

NeurIPS Conference 2020 Conference Paper

Taming Discrete Integration via the Boon of Dimensionality

• Jeffrey Dudek
• Dror Fried
• Kuldeep S Meel

Discrete integration is a fundamental problem in computer science that concerns the computation of discrete sums over exponentially large sets. Despite intense interest from researchers for over three decades, the design of scalable techniques for computing estimates with rigorous guarantees for discrete integration remains the holy grail. The key contribution of this work addresses this scalability challenge via an efficient reduction of discrete integration to model counting. The proposed reduction is achieved via a significant increase in the dimensionality that, contrary to conventional wisdom, leads to solving an instance of the relatively simpler problem of model counting. Building on the promising approach proposed by Chakraborty et al, our work overcomes the key weakness of their approach: a restriction to dyadic weights. We augment our proposed reduction, called DeWeight, with a state of the art efficient approximate model counter and perform detailed empirical analysis over benchmarks arising from neural network verification domains, an emerging application area of critical importance. DeWeight, to the best of our knowledge, is the first technique to compute estimates with provable guarantees for this class of benchmarks.

IJCAI Conference 2015 Conference Paper

From Weighted to Unweighted Model Counting

• Supratik Chakraborty
• Dror Fried
• Kuldeep S. Meel
• Moshe Y. Vardi

The recent surge of interest in reasoning about probabilistic graphical models has led to the development of various techniques for probabilistic reasoning. Of these, techniques based on weighted model counting are particularly interesting since they can potentially leverage recent advances in unweighted model counting and in propositional satisfiability solving. In this paper, we present a new approach to weighted model counting via reduction to unweighted model counting. Our reduction, which is polynomial-time and preserves the normal form (CNF/DNF) of the input formula, allows us to exploit advances in unweighted model counting to solve weighted model counting instances. Experiments with weighted model counters built using our reduction indicate that these counters performs much better than a state-of-the-art weighted model counter.

AAAI Conference 2015 Conference Paper

This Time the Robot Settles for a Cost: A Quantitative Approach to Temporal Logic Planning with Partial Satisfaction

• Morteza Lahijanian
• Shaull Almagor
• Dror Fried
• Lydia Kavraki
• Moshe Vardi

The specification of complex motion goals through temporal logics is increasingly favored in robotics to narrow the gap between task and motion planning. A major limiting factor of such logics, however, is their Boolean satisfaction condition. To relax this limitation, we introduce a method for quantifying the satisfaction of co-safe linear temporal logic specifications, and propose a planner that uses this method to synthesize robot trajectories with the optimal satisfaction value. The method assigns costs to violations of specifications from userdefined proposition costs. These violation costs define a distance to satisfaction and can be computed algorithmically using a weighted automaton. The planner utilizes this automaton and an abstraction of the robotic system to construct a product graph that captures all possible robot trajectories and their distances to satisfaction. Then, a plan with the minimum distance to satisfaction is generated by employing this graph as the high-level planner in a synergistic planning framework. The efficacy of the method is illustrated on a robot with unsatisfiable specifications in an office environment.

TCS Journal 2013 Journal Article

Complexity of Canadian traveler problem variants

• Dror Fried
• Solomon Eyal Shimony
• Amit Benbassat
• Cenny Wenner

SoCS Conference 2011 Conference Paper

Repeated-Task Canadian Traveler Problem

• Zahy Bnaya
• Ariel Felner
• Dror Fried
• Olga Maksin
• Solomon Eyal Shimony

In the Canadian Traveler Problem (CTP) a traveling agent is given a weighted graph, where some of the edges may be blocked, with a known probability. The agent needs to travel to a given goal. A solution for CTP is a policy, that has the smallest expected traversal cost. CTP is intractable. Previous work has focused on the case of a single agent. We generalize CTP to a repeated task version where a number of agents need to travel to the same goal, minimizing their combined travel cost. We provide optimal algorithms for the special case of disjoint path graphs. Based on a previous UCT-based approach for the single agent case, a framework is developed for the multi-agent case and four variants are given - two of which are based on the results for disjoint-path graphs. Empirical results show the benefits of the suggested framework and the resulting heuristics. For small graphs where we could compare to optimal policies, our approach achieves near optimal results at only a fraction of the computation cost.