MultiLanguage systems (ML systems) are formal systems allowing the use of multiple distinct logical languages. In this paper we introduce a class of ML systems which use a hierarchy of first order languages, each language containing names for the language below, and propose them as an alternative to modal logics. The motivations of our proposal are technical, epistemological and implementational. From a technical point of view, we prove, among other things, that the set of theorems of the most common modal logics can be embedded (under the obvious bijective mapping between a modal and a first order language) into that of the corresponding ML systems. Moreover, we show that ML systems have properties not holding for modal logics and argue that these properties are justified by our intuitions. This claim is motivated by the study of how ML systems can be used in the representation of beliefs (more generally, propositional attitudes) and provability, two areas where modal logics have been extensively used. Finally, from an implementation point of view, we argue that ML systems resemble closely the current practice in the computer representation of propositional attitudes and metatheoretic theorem proving.

The problem of context has a long tradition in different areas of artificial intelligence (AI). However, the issue of formalizing context has become a widely discussed issue only the late 80s, when J. McCarthy argued that formalizing context was a crucial step toward the solution of the problem of generality. Since then, two main formalizations have been proposed in AI: Propositional Logic of Context (PLC), by Buvac and Mason; and Local Models Semantics/MultiContext Systems (LMS/MCS), by Ghidini and Giunchiglia / Giunchiglia and Serafini.

We investigate the relationship between two well known formalizations of context: Propositional Logic of Context (PLC) [4], and Local Models Semantics (LMS) [13]. We start with a summary of the desiderata for a logic of context, mainly inspired by McCarthy's paper on generality in AI [15] and his notes on formalizing context [16]. We briey present LMS, and its axiomatization using MultiContext Systems (MCS) [14]. Then we present a revised (and simplied) version of PLC, and we show that local vocabularies { as they dened in [4] { are inessential in the semantics of PLC. The central part of the paper is the denition of a class of LMS (and its axiomatization in MCS, called MMCC), which is provably equivalent to the axiomatization of PLC as described in [4]. Finally, we

The aim of this work is to develop a resource-conscious Artificial Intelligence (AI) planning system, which allows for nondeterminism in the environment.

We propose to extend the ontology of logical AI to include approximate objects, approximate predicates and approximate theories. Besides the ontology we propose new representation and reasoning techniques, especially for formally relating different approximate theories of the same phenomena. Formal physical science theories treat well-defined objects in welldefined, or at least well-axiomatized, domains. Most logical AI theories have resembled scientific theories in this respect. However, human-level AI also requires reasoning about approximate entities, as we shall see. Approximate predicates can't have complete if-and-only-if definitions and usually don't even have de nite extensions. Some approximate concepts can be refined by learning more and some by defining more and some by both, but it isn't possible in general to make them well-defined. Approximate concepts are essential for representing common sense knowledge and doing common sense reasoning. Assertions involving appro...

Conformant planning is the problem of nding a sequence of actions that is guaranteed to achieve the goal for any possible initial state and nondeterministic behavior of the planning domain. In this paper we present a new approach to conformant planning. We propose an algorithm that returns the set of all conformant plans of minimal length if the problem admits a solution, otherwise it returns with failure. Our work is based on the planning via model checking paradigm, and relies on symbolic techniques such as Binary Decision Diagrams to compactly represent and eciently analyze the planning domain. The algorithm, called cmbp, has been implemented in the mbp planner. cmbp is strictly more expressive than the state of the art conformant planner cgp. Furthermore, an experimental evaluation suggests that cmbp is able to deal with uncertainties more eciently than cgp.