N. Roos, A Logic for Reasoning with Inconsistent Knowledge, Artificial Intelligence, Elsevier Science Publishers, 57, 1992, pp.69-103.  Home/Search   Document Details and Download   Summary   Related Articles   Check

.... now , this notion does not evolve as the reasoning progresses, making the logic incapable of addressing a key feature of active logic, namely, reasoning about the process of reasoning. Another aspect of Lesp erance and Levesque s logic left unaddressed is the problem of logical omniscience. In [ Roos, 1992 ] Roos discusses reasoning with inconsistent knowledge, and presents a particular logic for this. His logic has in common with active logic (and in contrast with paraconsistent logics) that the establishment of a contradiction is taken seriously, in that certain premises are then retracted. ....

N. Roos. A logic for reasoning with inconsistent knowledge. Artificial Intelligence, 57:69--103, 1992.

....belief is correct and what can be concluded assuming the second one is correct. Based on this motivation, we shall attempt to present several heuristics for handling an inconsistent knowledge base. Several other papers raise the possibility of an agent with an inconsistent knowledge base such as [Nir94, ED88, Del95, Roo92, IS92]. A general framework is developed by Delgrande for specification of logics of explicit belief [Del95] Although this approach enables the agent to believe in contradicting beliefs, it does not allow making deductions from an inconsistent set of beliefs. In this approach the agent may have ....

....paths, where each path is consistent, and a contradiction can only occur between different paths. Our approach, on the other hand, tries to handle the situation where there exists a contradiction on a given reasoning path. A logic for reasoning with inconsistent knowledge is described by Roos [Roo92]. In Roos logic a reliability relation is used to choose between incompatible assumptions. These choices are only made when a contradiction is derived. As we assume, as long as no contradiction is derived, the knowledge is assumed to be consistent. The idea to recover from inconsistent knowledge ....

Nico Roos. A logic for reasoning with inconsistent knowledge. Artificial Intelligence Journal, 57:69--103, 1992.

....the first belief is correct and what can be concluded assuming the second one is correct. Based on this motivation, we shall attempt to present heuristic for handling an inconsistent knowledge base. Several other papers raise the possibility of an agent with an inconsistent knowledge base such as [Nir94, ED88, Del95, Roo92, IS92] (see description below) A general framework is developed by Delgrande for specification of logics of explicit belief [Del95] Although this approach enables the agent to believe in contradicting beliefs, it does not allow making deductions from an inconsistent set of beliefs. In this approach ....

....paths, where each path is consistent, and a contradiction can only occur between different paths. Our approach, on the other hand, tries to handle the situation where there exists a contradiction on a given reasoning path. A logic for reasoning with inconsistent knowledge is described by Roos [Roo92]. In Roos logic a reliability relation is used to choose between incompatible assumptions. These choices are only made when a contradiction is derived. As we assume, as long as no contradiction is derived, the knowledge is assumed to be consistent. The idea to recover from inconsistent knowledge ....

Nico Roos. A logic for reasoning with inconsistent knowledge. Artificial Intelligence Journal, 57:69--103, 1992.

.... set, waiting for further analysis to appear) ffl As Gabbay and Hunter point out in [GaHu91] inconsistency in information is the norm (e.g. most systems have databases or knowledge bases where information may be obtained from different sources, or from not fully reliable sources, as commented in [Roos92]) and we should find ways to formalize it appropriately. Moreover, when an agent detects an inconsistency in its beliefs, it may interpret that fact as a signal to take external actions, such as asking the user, invoking a truth maintenance system, activating deactivating certain inference rules, ....

Roos, N., "A logic for reasoning with inconsistent knowledge", Artificial Intelligence 57, pp. 69-103, 1992.

....of argumentative structures, such as suggested by Pinkas and Loui [PL92] and universal properties of the logics that they induce. There are also a number of other argument based systems that have been proposed, including by Vreeswijk [Vre91, Vre97] Wagner [Wag91] Prakken [Pra93] Roos [Roo93] Fox et al. [FKA92, KAEF94, DFK96] Simari and Loui [SL92] Lin [Lin94] and Parsons [Par96] For a review of modelling argumentation in non classical logics see [PV99] These differ from argumentative logics in that they focus on defeasible reasoning: They incorporate defeasible, or ....

N Roos. A logic for reasoning with inconsistent knowledge. Artificial Intelligence, 57(1):69--104, 1993.

....piece(s) of information. The step S4 S4 consists of two substeps. a) Selecting a good CG from G. Normally, CG is the good with the highest priority in G , In case of ties, CG might be either one of those with the same highest priority (randomly selected) or their intersection (see [29] and [30]) This latter case means rejecting all the conflicting but equally credible information items. The result is not a good (it is not maximally consistent) and thus implies rejecting more assumptions than necessary to restore consistency. We believe that this could be avoided by simply considering G ....

Roos N. (1992), A Logic for Reasoning with Inconsistent Knowledge, in Artificial Intelligence», 57, pp. 69-103.

....We consider the discovery of a direct contradiction to be one of the chief ways for detecting miscommunication. Of course, the presence of a contradiction spells problems for a reasoning agent, and we are arguing here that a conversational agent must very much be a reasoner too. See (Miller 1993; Roos 1992; Perlis 1996) for more discussion of the problem of reasoning with inconsistent information. 5. Deixis: Without the ability to point, verbally, to this utterance and the present time, to ground a discussion in here and now, confusion can hardly be set aright. Time allows for the possibility ....

Roos, N. 1992. A logic for reasoning with inconsistent knowledge. Artificial Intelligence 57:69--103.

....objects that cannot have an a value. is actually needed here is non monotonic inheritance, i.e. a form of inheritance that allows new declarations to override previously derived information. We mention that several approaches to non monotonic inheritance have been proposed in the literature [7, 18, 33, 1, 23, 3] 12 . Clearly, both forms of inheritance are needed, as the appropriate form depends on the particular case. For example, i) let class Only car factory have a property produces with value domain Car, ii) let class Only boat factory have a property produces with value domain Boat, and (iii) ....

....of objects. In addition, we investigate inheritance from classes to instances through class membership (called, instance inheritance) Though instance inheritance from classes to tokens presents no particular problem, instance inheritance from meta classes to classes is not a trivial matter. 12 [18, 33, 3] do not present an object oriented framework. However, they consider partial ordering of declarations which can express defaults and exceptions in specialization hierarchies. ....

N. Roos, A Logic for Reasoning with Inconsistent Knowledge, Artificial Intelligence, Elsevier Science Publishers, 57, 69-103 (1992).

.... , if and only if it is entailed logically from p( and IFree( namely: p free f iff p( IFree( f Proposition: Each p consequence of is also a p Free consequence of . This is obvious since the p consequence uses a subset of the base used by ; p Free . Brewka (1989) see also Roos (1992)) has proposed a more adventurous approach to reason with inconsistent and layered knowledge bases, the idea is to take advantage of the stratification of the base to rank order the maximal consistent sub bases of and keep only the best ones, namely the so called preferred sub bases . Let = B ....

N. Roos (1992) A logic for reasoning with inconsistent knowledge. Artificial Intelligence, 57, 69-103.

....with contradictions How can a reasoning apparatus (person, robot, program) deal with contradictions There have been various proposals. Some, such as the paraconsistent logics surveyed in [1] aim to extract a trustworthy core of inferences while avoiding the contradictions. Others, such as [5, 15, 26], aim to detect and resolve contradictions. The latter are closer in spirit to the needs we are addressing here. Unlike the traditional view that abhors a contradiction and seeks at all costs to avoid such 5 and fears that CSR will come to naught (or to disaster) in their presence, the new ....

N. Roos. A logic for reasoning with inconsistent knowledge. Artificial Intelligence, 57:69--103, 1992.

.... followings are typical frameworks used for logical semantics of natural language: ffl Modal Logics [33] and their variations Intention and Action [30] Mental Space [15] Multiple World [42] 63] ffl Situation Theory [5, 6] ffl Mutual Knowledge [4] ffl Non monotonic reasoning [3] [57], 38, 45] ffl Grammar Formalisms The models of the above frameworks are basically constructed on possible world semantics. So we can treat these frameworks indirectly by simulating modal operators with term structures. In fact, some theorem provers [35] 16] 29, 28] for modal logic proves the ....

Nico Roos. "A Logic for Reasoning with Inconsistent Knowledge". Artificial Intelligence, 57:69--103, 1992.

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N. Roos, A Logic for Reasoning with Inconsistent Knowledge, Artificial Intelligence, Elsevier Science Publishers, 57, 1992, pp.69-103.

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