Author name cluster

Avraham Natan

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7 papers

2 author rows

AAMAS Conference 2026 Conference Paper

Diagnosing Faults in Deep Reinforcement Learning based Systems: Settings and Benchmarks

Avraham Natan
Roni Stern
Meir Kalech

Deep Reinforcement Learning (DRL) is often used to generate control policies for autonomous agents. These policies are trained to controlagentswhentheyoperatenormally. Thus, unexpectedfaults may cause agents controlled using these policies to fail. When this occurs, it is important to understand and explain the root cause of such failures. In this paper, we define this diagnosis problem under different settings and assumptions. We also provide a benchmark suite for evaluating algorithms for solving this problem based on environments from AI Gym, a popular DRL framework.

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AAAI Conference 2026 Conference Paper

Robust Multiagent Combinatorial Path Finding

Yehonatan Kidushim
Avraham Natan
Roni Stern
Meir Kalech

Consider a system of multiple physical agents tasked with collaboratively collecting a set of spatially distributed goals as quickly as possible while avoiding collisions with the environment and with each other. This type of problem, which involves Multi-Agent Path Finding (MAPF) and task allocation, is called Multi-Agent Combinatorial Path Finding (MCPF). Prior work on MCPF assumed each agent has a final goal it must reach, there are no orientation constraints on the agents' movements, and the agents will follow their planned actions as intended. These assumptions rarely hold in real physical robots, which limits the applicability of existing MCPF algorithms in practical applications. We propose the Robust CBSS framework, a robust planning approach that solves MCPF without the aforementioned simplifying assumptions, and provide two implementations: a baseline version (RCbssBase) and an efficient version (RCbssEff). RCbssEff generalizes the Conflict-Based Steiner Search (CBSS) algorithm, building on ideas from the p-Robust CBS algorithm and algorithms for solving the Equality Generalized Traveling Salesman Problem. We prove that RCbssEff is complete and can be configured to return optimal solutions. Experimental results on benchmark MCPF problems show that RCbssEff balances planning time, solution cost, and collision reduction compared to baselines.

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ECAI Conference 2023 Conference Paper

Blame Attribution for Multi-Agent Path Finding Execution Failures

Avraham Natan
Roni Stern
Meir Kalech

In Multi-Agent Systems (MAS), Multi-Agent Path Finding (MAPF) is the problem of finding a conflict-free plan for a group of agents from a set of starting points to a set of target points. Deviations from this plan are standard in real-world applications and may decrease overall system efficiency and even lead to accidents and deadlocks. In large MAS scenarios with physical robots, multiple faulty events occur over time, contributing to the overall degraded system performance. This raises the main problem we address in this work: how to attribute blame for a degraded MAS performance over a set of faulty events. We formally define this problem and propose using the Shapley values to solve it. Then, we propose an algorithm that efficiently approximates Shapley values by considering only some subsets of faulty events set. We analyze this algorithm theoretically and experimentally and demonstrate that it enables effectively trading off runtime for error.

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AAMAS Conference 2023 Conference Paper

Blame Attribution for Multi-Agent Pathfinding Execution Failures

Avraham Natan
Roni Stern
Meir Kalech

When executing large Multi-Agent Path Finding (MAPF) scenarios, faulty events can occur over time and contribute to the overall degraded system performance. This raises the problem of how to attribute blame over the set of faulty events. The first contribution of this paper is to define this problem and propose the well-known Shapley value for solving it. The second contribution is an efficient approach for approximating Shapley values that is inspired by diagnosis concepts.

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AIJ Journal 2023 Journal Article

Diagnosis of intermittent faults in Multi-Agent Systems: An SFL approach

Avraham Natan
Meir Kalech
Roman Barták

Multi-Agent Systems (MAS) can be found in a wide variety of applications, including industrial systems, transportation, software systems and more. In such systems, agents may experience faults that affect the performance of the whole system. However, faulty agents might not consistently experience their fault, but rather in certain conditions. For example, a robot with a faulty rotating mechanism will appear healthy if it is tasked to only move in a straight line. Those faults are called Intermittent Faults. Such faults may cause the entire system to fail, but not always. Previous work proposed diagnosis algorithms for MAS, assuming faulty agents persistently behave abnormally. To the best of our knowledge, intermittent faults in MAS have not been concretely explored. In this paper we formally present a novel problem called Diagnosis of Intermittent Faults in Multi-Agent Systems (DIFMAS): a group of agents are observed across multiple runs. In each run, the success/failure of the agents and the system is observed, aiming to explain all the failed runs by diagnosing which agent(s) are faulty. The contributions of this paper are: (1) formalizing DIFMAS as a Model-Based Diagnosis problem, (2) solving it by presenting a Spectrum-Based Fault Localization (SFL) based method, called Multi-Run SFL-based Diagnosis Algorithm (MRSD). Experiments demonstrate that MRSD's outperforms competing SFL-based algorithms. Moreover, the algorithm's performance increases if planned interactions are considered.

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AAAI Conference 2023 Conference Paper

Distributed Spectrum-Based Fault Localization

Avraham Natan
Roni Stern
Meir Kalech

Spectrum-Based Fault Localization (SFL) is a popular approach for diagnosing faulty systems. SFL algorithms are inherently centralized, where observations are collected and analyzed by a single diagnoser. Applying SFL to diagnose distributed systems is challenging, especially when communication is costly and there are privacy concerns. We propose two SFL-based algorithms that are designed for distributed systems: one for diagnosing a single faulty component and one for diagnosing multiple faults. We analyze these algorithms theoretically and empirically. Our analysis shows that the distributed SFL algorithms we developed output identical diagnoses to centralized SFL while preserving privacy.

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AAAI Conference 2022 Conference Paper

Model-Based Diagnosis of Multi-Agent Systems: A Survey

Meir Kalech
Avraham Natan

As systems involving multiple agents are increasingly deployed, there is a growing need to diagnose failures in such systems. Model-Based Diagnosis (MBD) is a well known AI technique to diagnose faults in systems. In this approach, a model of the diagnosed system is given, and the real system is observed. A failure is announced when the real system’s output contradicts the model’s expected output. The model then is used to deduce the defective components that explain the unexpected observation. MBD has been increasingly being deployed in distributed and multi-agent systems. In this survey, we summarize twenty years of research in the field of model-based diagnosis algorithms for MAS diagnosis. We depict three attributes that should be considered when examining MAS diagnosis: (1) The objective of the diagnosis. Either diagnosing faults in the MAS plans or diagnosing coordination faults. (2) Centralized vs. distributed. The diagnosis method could be applied either by a centralized agent or by the agents in a distributed manner. (3) Temporal vs. nontemporal. Temporal diagnosis is used to diagnose the MAS’s temporal behaviors, whereas non-temporal diagnosis is used to diagnose the conduct based on a single observation. We survey diverse studies in MBD of MAS based on these attributes, and provide novel research challenges in this field for the AI community.

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