What is abductive inference in AI?
Answers from top 6 papers
More filters
Papers (6) | Insight |
---|---|
184 Citations | Alas, abductive inference is curiously resistant to sharp distinctions, as I shall attempt to explain. |
60 Citations | Thus, abduction can be developed further as a ‘pure’ form of inference and this gives means for analysing and organizing the abductive search explicitly within the research community. |
Contra this assumption, we argue, first, that our belief in abduction's epistemic value is not and could not be justified a posteriori, and second, that attention to the roles experience plays in abductive justification supports taking abduction to be an a priori mode of inference. | |
01 Jan 2002 16 Citations | The experimental results reveal that the problem of partial abductive inference is difficult to solve by exact computation. |
The findings highlight the value of understanding explanation and abductive inference both as phenomena in their own right and for the insights they provide concerning foundational aspects of human cognition, such as representation, learning, and inference. | |
01 Jul 2014 15 Citations | Our formalization reveals new questions about possible extensions in abductive reasoning. |
Related Questions
How does generative AI produce false information?5 answersGenerative AI can produce false information due to biases and inaccuracies in the models. These inaccuracies can lead to serious consequences such as inaccurate decision-making, spread of misinformation, privacy violations, and legal liabilities. Models like OpenAI’s ChatGPT and GPT-4, which generate content based on prompts, can inadvertently create fake content, eroding trust in mainstream media and increasing public pressure for AI regulations globally. Additionally, generative models may generate nonsensical outputs when the true data distribution does not align with the model's assumptions, leading to errors in the generated content. To address these challenges, efforts are being made to verify generative AI outputs through data management practices to ensure correctness, transparency, and responsible use of AI.
What is the role of abductive reasoning in developing conceptual frameworks?4 answersAbductive reasoning plays a crucial role in developing conceptual frameworks. It allows for the explanation of phenomena by identifying the best fitting cause or hypothesis. By integrating abductive reasoning into the modeling process, it strengthens the practice of modeling as a key aspect of scientific reasoning. In the field of macroeconomics, abductive reasoning is used to deduce frameworks that can explain anomalous outcomes and guide policy decisions. In complex innovation processes, abductive reasoning helps innovators generate and evaluate hypotheses to make sense of puzzling facts and navigate through the complexities of product innovation. In the field of HRD, abductive reasoning is recognized as a typical but often unrecognized process used by scholars and practitioners to address difficult problems and bridge the gap between theory and practice. Overall, abductive reasoning enhances the development of conceptual frameworks by providing a systematic approach to explain phenomena and generate hypotheses.
What are the steps of abductive reasoning in developing conceptual frameworks?5 answersAbductive reasoning in developing conceptual frameworks involves several steps. First, it requires analyzing events and data to identify anomalies or puzzling facts. This is followed by generating and evaluating hypotheses to make sense of these anomalies. The next step involves backward reasoning from the outcome to deduce the framework that is compatible with the observed data. Abductive reasoning also involves formulating hypotheses for possible new products or concepts. In the context of logic programming, abductive reasoning can be used to infer explanations for incompatibility by computing hypotheses based on a general logic program. Additionally, abductive reasoning takes into account background knowledge and structured explananda, considering the agent's memory of specific facts and dealing with explananda having conceptual structure. Overall, abductive reasoning is a valuable tool for navigating complex situations and generating insights in various domains.
How should I verify what AI tells me?5 answersTo verify what AI tells you, there are several methods available. Verification and validation (V&V) is one approach used by software analysts to gain trust in AI systems. However, AI systems have unique features that make them challenging to check using conventional V&V methods. Nevertheless, alternative methods exist that enable the V&V of AI software. Additionally, trustworthiness is a crucial aspect of AI systems. Ensuring the trustworthiness of AI systems involves understanding what it means for an AI system to be trustworthy and taking actions to achieve this goal. Trust-based consent processes can also be used as an alternative to traditional consent mechanisms, where decisions to engage with AI systems are based on ongoing negotiation between stakeholders. Finally, a "trust but verify continuously" approach can be employed, which includes AI assurance activities throughout the system development lifecycle.
What is Bayesian probabilistic inference in AI?6 answers
What is probabilistic inference in artificial intelligence?5 answers