Learning concepts via instruction and expository texts is an important problem for modeling human learning and for making autonomous AI systems. This paper describes a computational model of the self-explanation effect, whereby conceptual knowledge is repaired by integrating and explaining new material. Our model represents conceptual knowledge with compositional model fragments, which are used to explain new material via model formulation. Preferences are computed over explanations and conceptual knowledge, along several dimensions. These preferences guide knowledge integration and question-answering. Our simulation learns about the human circulatory system, using facts from a circulatory system passage used in a previous cognitive psychology experiment. We analyze the simulation’s performance, showing that individual differences in sequences of models learned by students can be explained by different parameter settings in our model.

Model checking is one of the most effective techniques in automated system verification. Although this technique can handle complex verifications, model checking tools usually do not give any suggestions on how to repair inconsistent system models. In this paper, we show that approaches developed to update models of Computation Tree Logic (CTL) cannot deal with all kinds of changes. We introduce the concept of CTL model revision: an approach based on belief revision to handle system inconsistency in a static context.

Replanning via determinization is a recent, popular approach for onlineplanning in MDPs. In this paper we adapt this idea to classical,non-stochastic domains with partial information and sensing actions. At eachstep we generate a candidate plan which solves a classical planning probleminduced by the original problem. We execute this plan as long as it is safeto do so. When this is no longer the case, we replan.The classical planning problem we generate is based on the $T_0$ translation, in which the classical state captures the knowledge state of theagent. We overcome the non-determinism in sensing actions, and the large domain size introduced by $T_0$ by using state sampling. Our planner also employs a novel, lazy, regression-based method for querying the belief state.

Despite the importance of propositional logic in artificial intelligence, the notion of language independence in the propositional setting (not to be confound with syntax independence) has not received much attention so far. In this paper, we define language independence for a propositional operator as robustness w.r.t.symbol translation. We provide a number of characterizations results for such translations. We motivate the need to focus on symbol translations of restricted types, and identify several families of interest. We identify the computational complexity of recognizing symbol translations from those families. Finally, as a case study, we investigate the robustness of belief revision/merging operators w.r.t. translations of different types. It turns out that rational belief revision/merging operators are not guaranteed to offer the most basic (yet non-trivial) form of language independence; operators based on the Hamming distance do not suffer from this drawback but are less robust than operators based on the drastic distance.

Hierarchical task decomposition is a method used in many agent systems to organize agent knowledge. This work shows how the combination of a hierarchy and persistent assertions of knowledge can lead to difficulty in maintaining logical consistency in asserted knowledge. We explore the problematic consequences of persistent assumptions in the reasoning process and introduce novel potential solutions. Having implemented one of the possible solutions, Dynamic Hierarchical Justification, its effectiveness is demonstrated with an empirical analysis.

We propose a perspective on knowledge compilation which calls for analyzing different compilation approaches according to two key dimensions: the succinctness of the target compilation language, and the class of queries and transformations that the language supports in polytime. We then provide a knowledge compilation map, which analyzes a large number of existing target compilation languages according to their succinctness and their polytime transformations and queries. We argue that such analysis is necessary for placing new compilation approaches within the context of existing ones. We also go beyond classical, flat target compilation languages based on CNF and DNF, and consider a richer, nested class based on directed acyclic graphs (such as OBDDs), which we show to include a relatively large number of target compilation languages.

We investigate the space efficiency of a Propositional Knowledge Representation (PKR) formalism. Intuitively, the space efficiency of a formalism F in representing a certain piece of knowledge A, is the size of the shortest formula of F that represents A. In this paper we assume that knowledge is either a set of propositional interpretations (models) or a set of propositional formulae (theorems). We provide a formal way of talking about the relative ability of PKR formalisms to compactly represent a set of models or a set of theorems. We introduce two new compactness measures, the corresponding classes, and show that the relative space efficiency of a PKR formalism in representing models/theorems is directly related to such classes. In particular, we consider formalisms for nonmonotonic reasoning, such as circumscription and default logic, as well as belief revision operators and the stable model semantics for logic programs with negation. One interesting result is that formalisms with the same time complexity do not necessarily belong to the same space efficiency class.

The aim of Belief Merging is to aggregate possibly conflicting pieces of information issued from different sources. The quality of the resulting set is usually considered in terms of a closeness criterion between the resulting belief set and the initial belief sets. The notion of distance between belief sets is thus a crucial issue when we face the merging problem. The aim of this paper is twofold: introducing a syntactical way to calculate distances and proposing the use of a distance based on prime implicants and prime implicates that considers the importance of each propositional symbol in the belief set.

Conformant planning can be formulated as a path-finding problem in belief space where the two main challenges are the heuristics to guide the search, and the representation and update of beliefs. In the translation-based approach recently introduced by Palacios and Geffner, the two aspects are handled together by translating conformant problems into classical ones that are solved with classical planners. While competitive with state-of-the-art methods, the translation-based approach runs however into three difficulties. First, complete translations are expensive for problems with high width; second, incomplete translations can generate infinite heuristic values for problems that are solvable; and third, aspects that are specific to the conformant setting, such as the cardinality of beliefs, are not accounted for. In this work, we build on the translation-based approach but not for solving conformant problems with a classical planner but for deriving heuristics and computing beliefs in the context of a standard belief-space planner. For this, a novel translation KSi is introduced that is always complete, but which is sound for problems with width bounded by i. A new conformant planner, called T1, builds then on this translation for i=1, extending the heuristic that results with a second heuristic obtained from invariant "oneof expressions". A number of experiments is performed to compare T1 with state-of-the-art conformant planners.

The paper develops a formal model for interpreting vague languages based on a variant of "supervaluation" semantics. Two modes of semantic variability are modelled, corresponding to different aspects of vagueness: one mode arises where there can be multiple definitions of a term; and the other relates to the threshold of applicability of a vague term with respect to the magnitude of relevant observable values. The truth of a proposition depends on both the possible world and the "precisification" with respect to which it is evaluated. Structures representing possible worlds and precisifications are both specified in terms of primitive functions representing observable measurements, so that the semantics is grounded upon an underlying theory of physical reality. On the basis of this semantics, the acceptability of a proposition to an agent is characterised in terms of a combination of agent's beliefs about the world and their attitude to admissible interpretations of vague predicates.