men. It is possible to extend the reasoning encoded in the above programs, in order to write a general program for n **wise men, by exploiting the nesting feature of the**
social conditions in such a way that reasoning on both the content and the temporal sequence of the wise men's statements is enabled.

## 8 Related Work

Contextual Reasoning **- As pointed out in Section 7, a relationship exists between** our work and (Ghidini and Giunchiglia 2001), where the Local Model Semantics
(LMS) is proposed to reason about contexts.

A survey covering the use of contexts in many fields of Artificial Intelligence can be found in (Br´ezillon 1999). An approach concerning contextual **reasoning and** agent-based systems can be found in (De Saeger and Shimojima 2006).

In (McCarthy 1993), the author discusses the notion of context **in Artificial Intelligence in order to solve the problem of** generality**, that is every logic theory is**
valid within the bounds of a definite context and it is possible to design a **more** general context where such a theory is not valid anymore.

Other approaches have been proposed in (Buvaˇc and Mason 1993) and (Ghidini and Giunchiglia 2001).

Moreover, these two works are compared in (Serafini and Bouquet 2004). In the former work, the authors introduce the Propositional Logic of Context**, a modal logic**
aiming at formalizing McCarthy's ideas.

An approach which is more closely related to ours is proposed in (Ghidini and Giunchiglia 2001),
where the authors consider a set of logic languages, each representing a different context, and a suitable semantics is used to select among sets of local **models, i.e.** models pertaining to a single language, those which satisfy a given compatibility condition. Moreover, a proof-theoretical framework for contextual reasoning, called Multi-Context Systems**, is introduced where the notions of locality and compatibility are respectively captured by** inference **rules, whose scope is the single language,**
and bridge **rules establishing relationships among different languages.**
Observe that, in the previous section, we have shown that such a machinery can be represented by social rules where the body includes only member-selectioncondition-based SCs, each corresponding to a different context to be included into the reasoning task. As a consequence, we argue that social rules **are more general**
than bridge rules, since the former provide also (possibly nested) cardinal-selectioncondition-based SCs. Since our work is not aimed to reason on contexts, a direct comparison with (Ghidini and Giunchiglia 2001) cannot be done, although some correspondences may be found between the model-theoretical formalizations of both the Local Model Semantics and the Social Semantics. In particular, we feel that the latter could be easily adapted to fully enable contextual reasoning inside logic programming. This is left for future work. Logic-based Multi-Agent Systems **- A related approach, where the semantics of a**
collection of abductive logic agents is given in terms of the stability of their interaction can be found in (Bracciali et al. 2004) where the authors define **the semantics**
of a multi-agent system via a definition of stability on the set of all actions per-