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| 2. |
- Devaraj, Lokesh, et al.
(författare)
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Improvements proposed to noisy-OR derivatives for multi-causal analysis: A case study of simultaneous electromagnetic disturbances
- 2024
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Ingår i: International Journal of Approximate Reasoning. - 0888-613X .- 1873-4731. ; 164
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Tidskriftsartikel (refereegranskat)abstract
- In multi-causal analysis, the independence of causal influence (ICI) assumed by the noisy-OR (NOR) model can be used to predict the probability of the effect when several causes are present simultaneously, and to identify (when it fails) inter-causal dependence (ICD) between them. The latter is possible only if the probability of observing the multi-causal effect is available for comparison with a corresponding NOR estimate. Using electromagnetic interference in an integrated circuit as a case study, the data corresponding to the probabilities of observing failures (effect) due to the injection of individual (single cause) and simultaneous electromagnetic disturbances having different frequencies (multiple causes) were collected. This data is initially used to evaluate the NOR model and its existing derivatives, which have been proposed to reduce the error in predictions for higher-order multi-causal interactions that make use of the available information on lower-order interactions. Then, to address the identified limitations of the NOR and its existing derivatives, a new deterministic model called Super-NOR is proposed, which is based on correction factors estimated from the available ICD information.
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| 3. |
- Doherty, Patrick, 1957-, et al.
(författare)
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Rough set reasoning using answer set programs
- 2021
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Ingår i: International Journal of Approximate Reasoning. - : Elsevier. - 0888-613X .- 1873-4731. ; 130:March, s. 126-149
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Tidskriftsartikel (refereegranskat)abstract
- Reasoning about uncertainty is one of the main cornerstones of Knowledge Representation. Formal representations of uncertainty are numerous and highly varied due to different types of uncertainty intended to be modeled such as vagueness, imprecision and incompleteness. There is a rich body of theoretical results that has been generated for many of these approaches. It is often the case though, that pragmatic tools for reasoning with uncertainty lag behind this rich body of theoretical results. Rough set theory is one such approach for modeling incompleteness and imprecision based on indiscernibility and its generalizations. In this paper, we provide a pragmatic tool for constructively reasoning with generalized rough set approximations that is based on the use of Answer Set Programming (Asp). We provide an interpretation of answer sets as (generalized) approximations of crisp sets (when possible) and show how to use Asp solvers as a tool for reasoning about (generalized) rough set approximations situated in realistic knowledge bases. The paper includes generic Asp templates for doing this and also provides a case study showing how these techniques can be used to generate reducts for incomplete information systems. Complete, ready to run clingo Asp code is provided in the Appendix, for all programs considered. These can be executed for validation purposes in the clingo Asp solver.
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| 4. |
- Kampik, Timotheus, 1989-, et al.
(författare)
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Change in quantitative bipolar argumentation : sufficient, necessary, and counterfactual explanations
- 2024
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Ingår i: International Journal of Approximate Reasoning. - Amsterdam : Elsevier. - 0888-613X .- 1873-4731. ; 164
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Tidskriftsartikel (refereegranskat)abstract
- This paper presents a formal approach to explaining change of inference in Quantitative Bipolar Argumentation Frameworks (QBAFs). When drawing conclusions from a QBAF and updating the QBAF to then again draw conclusions (and so on), our approach traces changes – which we call strength inconsistencies – in the partial order over argument strengths that a semantics establishes on some arguments of interest, called topic arguments. We trace the causes of strength inconsistencies to specific arguments, which then serve as explanations. We identify sufficient, necessary, and counterfactual explanations for strength inconsistencies and show that strength inconsistency explanations exist if and only if an update leads to strength inconsistency. We define a heuristic-based approach to facilitate the search for strength inconsistency explanations, for which we also provide an implementation.
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| 5. |
- Kampik, Timotheus, 1989-, et al.
(författare)
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Ensuring reference independence and cautious monotony in abstract argumentation
- 2022
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Ingår i: International Journal of Approximate Reasoning. - : Elsevier. - 0888-613X .- 1873-4731. ; 140, s. 173-210
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Tidskriftsartikel (refereegranskat)abstract
- In the symbolic artificial intelligence community, abstract argumentation with its semantics, i.e. approaches for defining sets of valid conclusions (extensions) that can be derived from argumentation graphs, is considered a promising method for non-monotonic reasoning. However, from a sequential perspective, abstract argumentation-based decision-making processes typically do not guarantee an alignment with common formal notions to assess consistency; in particular, abstract argumentation can, in itself, not enforce the satisfaction of relational principles such as reference independence (based on a key principle of microeconomic theory) and cautious monotony. In this paper, we address this issue by introducing different approaches to ensuring reference independence and cautious monotony in sequential argumentation: a reductionist, an expansionist, and an extension-selecting approach. The first two approaches are generically applicable, but may require comprehensive changes to the corresponding argumentation framework. In contrast, the latter approach guarantees that an extension of the corresponding argumentation framework can be selected to satisfy the relational principle by requiring that the used argumentation semantics is weakly reference independent or weakly cautiously monotonous, respectively, and also satisfies some additional straightforward principles. To highlight the relevance of the approach, we illustrate how the extension-selecting approach to reference independent argumentation can be applied to model (boundedly) rational economic decision-making.
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| 6. |
- Lachmann, Jon, et al.
(författare)
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A subsampling approach for Bayesian model selection
- 2022
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Ingår i: International Journal of Approximate Reasoning. - : Elsevier BV. - 0888-613X .- 1873-4731. ; 151, s. 33-63
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Tidskriftsartikel (refereegranskat)abstract
- It is common practice to use Laplace approximations to decrease the computational burden when computing the marginal likelihoods in Bayesian versions of generalised linear models (GLM). Marginal likelihoods combined with model priors are then used in different search algorithms to compute the posterior marginal probabilities of models and individual covariates. This allows performing Bayesian model selection and model averaging. For large sample sizes, even the Laplace approximation becomes computationally challenging because the optimisation routine involved needs to evaluate the likelihood on the full dataset in multiple iterations. As a consequence, the algorithm is not scalable for large datasets. To address this problem, we suggest using stochastic optimisation approaches, which only use a subsample of the data for each iteration. We combine stochastic optimisation with Markov chain Monte Carlo (MCMC) based methods for Bayesian model selection and provide some theoretical results on the convergence of the estimates for the resulting time-inhomogeneous MCMC. Finally, we report results from experiments illustrating the performance of the proposed algorithm.
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| 7. |
- Tiger, Mattias, 1989-, et al.
(författare)
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Incremental Reasoning in Probabilistic Signal Temporal Logic
- 2020
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Ingår i: International Journal of Approximate Reasoning. - : Elsevier. - 0888-613X .- 1873-4731. ; 119, s. 325-352
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Tidskriftsartikel (refereegranskat)abstract
- Robot safety is of growing concern given recent developments in intelligent autonomous systems. For complex agents operating in uncertain, complex and rapidly-changing environments it is difficult to guarantee safety without imposing unrealistic assumptions and restrictions. It is therefore necessary to complement traditional formal verification with monitoring of the running system after deployment. Runtime verification can be used to monitor that an agent behaves according to a formal specification. The specification can contain safety-related requirements and assumptions about the environment, environment-agent interactions and agent-agent interactions. A key problem is the uncertain and changing nature of the environment. This necessitates requirements on how probable a certain outcome is and on predictions of future states. We propose Probabilistic Signal Temporal Logic (ProbSTL) by extending Signal Temporal Logic with a sub-language to allow statements over probabilities, observations and predictions. We further introduce and prove the correctness of the incremental stream reasoning technique progression over well-formed formulas in ProbSTL. Experimental evaluations demonstrate the applicability and benefits of ProbSTL for robot safety.
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