“Affective Taxis: A New Approach to AI Alignment”

by | May 27, 2025 | AI

arXiv:2505.17024v1 Announce Type: new
Abstract: AI alignment is a field of research that aims to develop methods to ensure that agents always behave in a manner aligned with (i.e. consistently with) the goals and values of their human operators, no matter their level of capability. This paper proposes an affectivist approach to the alignment problem, re-framing the concepts of goals and values in terms of affective taxis, and explaining the emergence of affective valence by appealing to recent work in evolutionary-developmental and computational neuroscience. We review the state of the art and, building on this work, we propose a computational model of affect based on taxis navigation. We discuss evidence in a tractable model organism that our model reflects aspects of biological taxis navigation. We conclude with a discussion of the role of affective taxis in AI alignment.

Expert Commentary: The Affectivist Approach to AI Alignment

In the realm of artificial intelligence (AI) research, the concept of AI alignment has become a key area of focus in recent years. The goal of AI alignment is to ensure that autonomous agents, such as AI systems, consistently act in ways that are in line with the goals and values of their human creators. This is crucial for maintaining control and ensuring the safe and ethical use of AI technology.

This paper introduces an innovative approach to the AI alignment problem known as the affectivist approach. By reframing the concepts of goals and values in terms of affective taxis, the authors propose a new perspective on understanding how AI systems can be aligned with human intentions. Affective taxis refers to the inherent drive or motivation that guides an agent’s actions, much like the concept of emotional valence in human decision-making.

Multidisciplinary Insights

What sets the affectivist approach apart is its interdisciplinary nature, drawing on insights from evolutionary-developmental and computational neuroscience. By exploring how affective valence can emerge in AI systems through computational models based on taxis navigation, the authors shed light on the complex interplay between emotions, motivations, and decision-making processes in autonomous agents.

The incorporation of evidence from biological taxis navigation in model organisms further strengthens the validity of the proposed computational model of affect. This multi-disciplinary approach not only enriches our understanding of AI alignment but also opens up new avenues for research at the intersection of neuroscience, psychology, and artificial intelligence.

Implications for AI Alignment

By emphasizing the role of affective taxis in shaping the behavior of AI systems, this paper highlights the importance of integrating emotional intelligence and ethical considerations into the design and development of autonomous agents. Understanding how affective valence can be harnessed to align AI with human values is crucial for advancing the field of AI alignment and ensuring the responsible use of AI technology.

Overall, the affectivist approach presents a novel and promising framework for addressing the AI alignment problem, blending insights from multiple disciplines to tackle the complex challenge of aligning AI with human intentions. As research in this area continues to evolve, it is clear that a multi-disciplinary approach will be essential for shaping the future of artificial intelligence.

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“Dynamic Candidate Selection for Improved Routing Algorithms”

by | May 24, 2025 | AI

arXiv:2505.15862v1 Announce Type: new
Abstract: Algorithms designed for routing problems typically rely on high-quality candidate edges to guide their search, aiming to reduce the search space and enhance the search efficiency. However, many existing algorithms, like the classical Lin-Kernighan-Helsgaun (LKH) algorithm for the Traveling Salesman Problem (TSP), often use predetermined candidate edges that remain static throughout local searches. This rigidity could cause the algorithm to get trapped in local optima, limiting its potential to find better solutions. To address this issue, we propose expanding the candidate sets to include other promising edges, providing them an opportunity for selection. Specifically, we incorporate multi-armed bandit models to dynamically select the most suitable candidate edges in each iteration, enabling LKH to make smarter choices and lead to improved solutions. Extensive experiments on multiple TSP benchmarks show the excellent performance of our method. Moreover, we employ this bandit-based method to LKH-3, an extension of LKH tailored for solving various TSP variant problems, and our method also significantly enhances LKH-3’s performance across typical TSP variants.

Expert Commentary: Enhancing Routing Algorithms with Multi-Armed Bandit Models

In the field of algorithm design for routing problems, the use of candidate edges plays a crucial role in guiding search processes to find optimal solutions efficiently. However, traditional algorithms often suffer from using static candidate edges, which can lead to being trapped in local optima and limiting their ability to find better solutions.

One innovative approach to address this challenge is the incorporation of multi-armed bandit models into routing algorithms, as proposed in this study. By dynamically selecting promising candidate edges in each iteration, algorithms like LKH can make smarter choices and potentially lead to improved solutions. This dynamic selection process adds a layer of adaptability and flexibility to the algorithm, allowing it to explore a wider range of possibilities and avoid being stuck in suboptimal solutions.

The use of multi-armed bandit models in routing algorithms highlights the multi-disciplinary nature of this research, combining concepts from algorithm design, optimization, and machine learning. By leveraging insights from different fields, researchers can develop more robust and efficient algorithms that can adapt to changing environments and problem characteristics.

The results of the experiments conducted on multiple TSP benchmarks demonstrate the effectiveness of incorporating multi-armed bandit models into the LKH algorithm. Furthermore, extending this approach to LKH-3 and other TSP variant problems showcases the potential for this method to enhance the performance of a wide range of routing algorithms.

Overall, this study opens up new possibilities for improving routing algorithms by integrating techniques from diverse disciplines, highlighting the importance of interdisciplinary research in advancing the field of algorithm design and optimization.

Reference: arXiv:2505.15862v1

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“Dynamic Post-Shielding Framework for Enforcing $omega$-Regular Correctness Properties”

by | May 23, 2025 | AI

arXiv:2505.14689v1 Announce Type: new
Abstract: This paper presents a novel dynamic post-shielding framework that enforces the full class of $omega$-regular correctness properties over pre-computed probabilistic policies. This constitutes a paradigm shift from the predominant setting of safety-shielding — i.e., ensuring that nothing bad ever happens — to a shielding process that additionally enforces liveness — i.e., ensures that something good eventually happens. At the core, our method uses Strategy-Template-based Adaptive Runtime Shields (STARs), which leverage permissive strategy templates to enable post-shielding with minimal interference. As its main feature, STARs introduce a mechanism to dynamically control interference, allowing a tunable enforcement parameter to balance formal obligations and task-specific behavior at runtime. This allows to trigger more aggressive enforcement when needed, while allowing for optimized policy choices otherwise. In addition, STARs support runtime adaptation to changing specifications or actuator failures, making them especially suited for cyber-physical applications. We evaluate STARs on a mobile robot benchmark to demonstrate their controllable interference when enforcing (incrementally updated) $omega$-regular correctness properties over learned probabilistic policies.

Expert Commentary on Dynamic Post-Shielding Framework

The concept of a dynamic post-shielding framework that enforces both safety and liveness properties over pre-computed probabilistic policies represents a significant advancement in the field of autonomous systems and robotics. Traditionally, safety-shielding has been the primary focus, ensuring that systems never enter into undesirable states. However, this new framework expands beyond safety to include liveness properties, guaranteeing that the system eventually reaches desired states or goals.

The use of Strategy-Template-based Adaptive Runtime Shields (STARs) is a key innovation in this framework. By leveraging permissive strategy templates, STARs are able to enforce post-shielding with minimal interference, allowing for a better balance between formal correctness guarantees and task-specific behaviors. The ability to dynamically control interference with a tunable enforcement parameter is particularly noteworthy, as it provides flexibility in how aggressively the system enforces correctness properties based on the current situation.

Furthermore, the support for runtime adaptation in STARs is a crucial feature, especially in cyber-physical applications where specifications may change or components may fail. The ability to dynamically adjust to these changes ensures the continued reliability and effectiveness of the system over time.

The evaluation of STARs on a mobile robot benchmark underscores the practical applicability of this framework. By demonstrating controllable interference when enforcing $omega$-regular correctness properties over learned probabilistic policies, the study showcases the effectiveness of STARs in real-world scenarios.

Overall, the multi-disciplinary nature of this research, combining concepts from control theory, formal methods, and robotics, highlights the importance of integrating diverse expertise to push the boundaries of autonomous systems and ensure their safety and reliability in complex environments.

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