Abstract. The use of artificial intelligence to represent and reason about metabolic networks has been widely investigated due to the complexity of their imbrication. Its main goal is to determine the catalytic role of genomes and their interference in the process. This paper presents a log- ical model for metabolic pathways capable of describing both positive and negative reactions (activations and inhibitions) based on a fragment of first order logic. We also present a translation procedure that aims to transform first order formulas into quantifier free formulas, creating an efficient **automated** deduction method allowing us to predict results by deduction and infer reactions and proteins states by abductive **reasoning**. Keywords: Metabolic pathways, logical model, inhibition, **automated** **reasoning**.

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Abstract. The use of artificial intelligence to represent and reason about metabolic networks has been widely investigated due to the complexity of their imbrication. Its main goal is to determine the catalytic role of genomes and their interference in the process. This paper presents a log- ical model for metabolic pathways capable of describing both positive and negative reactions (activations and inhibitions) based on a fragment of first order logic. We also present a translation procedure that aims to transform first order formulas into quantifier free formulas, creating an efficient **automated** deduction method allowing us to predict results by deduction and infer reactions and proteins states by abductive **reasoning**. Keywords: Metabolic pathways, logical model, inhibition, **automated** **reasoning**.

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Chapter 4
Simulating SMT-solving in the sequent calculus
An important area of **automated** **reasoning** is about satisﬁability problems and how to solve them. The most basic satisﬁability problem is propositional/Boolean satisﬁability (SAT), where the goal is to decide whether a condition about Boolean variables (e.g. a formula over Boolean variables formed with logical connectives), can be made true by choosing true/false values for its variables. The main techniques for solving propositional SAT-problems are based on the DPLL procedure (for Davis-Putnam-Logemann-Loveland) [ DP60 , DLL62 ], cutting the exploration of the exponential number of possible truth assignments in a complete way.

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Given this kind of reasoning scheme, the central element in AMES' simulation procedure is naturally the individual qualitative state analysis: the process by which AMES[r]

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L’archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d’enseignemen[r]

• Possibilistic logic relies on the minimal specificity principle that helps selecting a unique ordering of interpretations, in the spirit of rational closure [32] or system Z of Pearl [36] , while the relative certainty logic approach does not appeal to it and is more cautious.
The form of possibilistic logic closest to the conditional logic encoding of po-bases is SPL, when weights are symbolic representations of ill-known certainty values with constraints on their ranking. One important contribution of this paper is the comparison between SPL and partially ordered bases, in the form of partial mutual translations. This link is important because procedures for **automated** **reasoning** from po-bases could be implemented in possibilistic logic, which is easier to exploit than a full-fledged conditional logic. This is our topic of current investigation [13] . This work also has potential applications for the revision and the fusion of beliefs, as well as preference modeling [23] .

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Abstract
Default **reasoning** and interpolation are two impor- tant forms of commonsense rule-based **reasoning**. The former allows us to draw conclusions from in- completely specified states, by making assumptions on normality, whereas the latter allows us to draw conclusions from states that are not explicitly cov- ered by any of the available rules. Although both approaches have received considerable attention in the literature, it is at present not well understood how they can be combined to draw reasonable con- clusions from incompletely specified states and in- complete rule bases. In this paper, we introduce an inference system for interpolating default rules, based on a geometric semantics in which normal- ity is related to spatial density and interpolation is related to geometric betweenness. We view default rules and information on the betweenness of natu- ral categories as particular types of constraints on qualitative representations of G¨ardenfors concep- tual spaces. We propose an axiomatization, extend- ing the well-known System P, and show its sound- ness and completeness w.r.t. the proposed seman- tics. Subsequently, we explore how our extension of preferential **reasoning** can be further refined by adapting two classical approaches for handling the irrelevance problem in default **reasoning**: rational closure and conditional entailment.

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Unfortunately, reentrant locks are inherently problematic for separation-logic rea- soning, which tries to completely replace “negative” **reasoning** about the absence of aliasing by “positive” **reasoning** about the possession of access permissions. The prob- lem is that a verification system for reentrant locks has to distinguish between initial lock entries and reentries, because only after initial entries is it sound to assume a lock’s resource invariant. This means that initial lock entries need a precondition requiring that the current thread does not already hold the acquired lock. Establishing this precondi- tion boils down to proving that the acquired lock does not alias a currently held lock, i.e., to proving absence of aliasing.

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The existence of a unique solution is not always granted: for instance in the Boolean setting, the solution may not exist [ 10 ].
Analogical Inference. In this perspective, analogical **reasoning** can be viewed as a way to infer new plausible information, starting from observed analogical pro- portions. The analogical jump is an unsound inference principle postulating that, given 4 vectors a, b, c, d such that the proportion holds on some components, then it should also hold on the remaining ones. This can be stated as (where a = (a 1 , a 2 , · · · a n ), and J ⊂ [1, n]):

Egraph operations We now describe the operations that transform the Egraph during the model building phase; they each correspond to an exported primitive in the Egraph API. Any node participating to one of the Egraph operations must be registered. Until then the node is dormant; separating node creation and registration avoids **reasoning** on a part of formula that is not needed: e.g., with ite (c, t, e) the nodes t (resp. e) could be dormant until we know that c has value ⊺ (resp. )). Four kinds of Egraph operations can be applied to an Egraph E: Merge: Two registered nodes n 1 and n 2 have their equivalence classes merged, the resulting

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1 Introduction
Probabilistic couplings [ 9 , 7 , 10 ] are a powerful mathematical tool for **reasoning** about pairs of probabilistic processes: streams of values that evolve randomly according to some rule. While the two processes may be difficult to analyze independently, a probabilistic coupling arranges processes {u i }, {v i } in the same space—for the simplest form of couplings, by viewing the pair of processes as randomly evolving pairs of values {(u i , v i )}—while ensuring certain coupling requirements. In this way, a coupling can coordinate the samples between the two processes so that the coupled process satisfies certain properties.

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Keywords: mobile **reasoning**, ubiquitous semantic web, client-side **reasoning**
1 Introduction
To address scalability concerns that arise with high numbers of simultaneous re- quests, web application designers dispose of several tools, among which caching static data and deferring code execution from the server to the client side. But even if in average, client processing resources augment at a fast pace, they re- main heterogeneous and in some cases, too limited to execute heavy calculation processes. Adaptivity and flexibility depending on the client resources is there- fore necessary. This concern also arises with semantic web technologies: solving SPARQL queries for a large number of clients can require heavy **reasoning** pro- cesses and cause endpoints unavailability. Client-side **reasoning** is therefore to consider while designing a semantics-enabled web application. Moreover, mobile devices and smart appliances provide an opportunity for semantic technologies to exploit the paradigm of ubiquitous computing and provide knowledge sharing and **reasoning** facilities wrt. standards on different devices. But again, their di- versity and heterogeneity require the ability to defer **reasoning** tasks on a client or to perform them on the server if the client is unable to handle them.

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The successive approximation cliche use now also has the approximation role filled, and an explanation of it would now proceed in a slightly different manner. The existenc[r]

2 to PSPACE) – under the assumption of completeness for PSPACE, of course.
8 Conclusion
In this paper, we have given a “logic” for paraconsistent rea- soning where comma is considered as a genuine connective. We have presented some properties of this logic, and we have shown that it allows to express, in a unified language, several problems of **reasoning** with inconsistent belief bases, non- monotonic inference, belief revision and belief merging. We have also provided some translatability results and some com- plexity results. Finally, we have given an alternative seman- tics for the comma connective.

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3 **Reasoning** about Actions
We now put ATLEA to work and demonstrate its usefulness in **reasoning** about mul- tiagent actions. We start by encoding in ATLEA Reiter’s action descriptions in terms of complete conditions for the executability and the effects of actions. We build on the mapping of Reiter’s solution to the frame problem into dynamic epistemic logics with assignments as done in [7]. We take the multiagent context into account by integrat- ing ideas stemming from logics of propositional control. There, the set of propositional variables is partitioned among the agents, and an agent controlling a variable is the only one able to change its truth value [26].

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bilistic logic. Proc. 13th Int. Conf. on Principles of Knowledge Representation and **Reasoning** (KR’12), (G. Brewka, Th. Eiter, S. A. McIlraith, eds.), Roma, June 10-14, AAAI Press, 519–529.
6. Cholvy, L. (2011). How strong can an agent believe reported information? Proc. 11th Europ. Conf. on Symbolic and Quantitative Approaches to **Reasoning** with Uncertainty. (ECSQARU’11), (W. Liu, ed.), Belfast, June 29-July 1, LNCS 6717, Springer, 386–397.

A condition is retroactive when the occurrence of the conditioned legal arrangement (for suspensive conditions) or its cancellation (for resolutive conditions) is assumed to take place a[r]

In this paper, we are concerned with the deductive aspect of **reasoning** (cf. [4, 31, 9, 1] for works in the same framework). Following Pinkas and Loui's anal- ysis [32], it is convenient to see coherence-based nonmonotonic entailment as a two-steps procedure which rst restores the coherence by generating and selecting preferred belief states (generation mechanism) and then manages these multiple states in order to conclude using classical logic (entailment principle). For in- stance, the following kind of inference is considered in [1]: \The belief base E infers i is classically inferred by all the preferred consistent subsets of E ".

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The key assumption is that messages of the same types should start with similar headers even if the message contents are totally different.
5 **Automated** protocol clustering
Using the previously defined metrics, we derive an unsupervised clustering method that combines two clustering methods in order to determine the number of differ- ent messages types. The first technique is a new method relying on unsupervised support vector clustering [28]. The second method is based on the well known agglomerative nearest neighbor method [29]. This last technique considers each data point as an individual cluster. The two clusters with the smallest inter- distance are merged into one. Then, this step is repeated until the smallest inter-distance is higher than a threshold t.

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Contributions A language-independent logic and a proof system suitable for stating and proving the equivalence of concrete and of symbolic programs as well as of terminating and non-term[r]