We present a conflict-based approach to diagnosing Discrete Event Systems (DES) which generalises Reiter's Diagnose algorithm to a much broader class of problems. This approach obviates the need to explicitly reconstruct the system's behaviors that are consistent with the observation, as is typical of existing DES diagnosis algorithms. Instead, our algorithm explores the space of diagnosis hypotheses, testing hypotheses for consistency, and generating conflicts which rule out successors and other portions of the search space. Under relatively mild assumptions, our algorithm correctly computes the set of preferred diagnosis candidates. We investigate efficient symbolic representations of the hypotheses space and provide a SAT-based implementation of this framework which is used to address a real-world problem in processing alarms for a power transmission system.

Abstraction has been used in search and planning from the very beginning of AI. Many different methods and formalisms for abstraction have been proposed in the literature but they have been designed from various points of view and with varying purposes. Hence, these methods have been notoriously difficult to analyse and compare in a structured way. In order to improve upon this situation, we present a coherent and flexible framework for modelling abstraction (and abstraction-like) methods based on transformations on labelled graphs. Transformations can have certain method properties that are inherent in the abstraction methods and describe their fundamental modelling characteristics, and they can have certain instance properties that describe algorithmic and computational characteristics of problem instances. The usefulness of the framework is demonstrated by applying it to problems in both search and planning. First, we show that we can capture many search abstraction concepts (such as avoidance of backtracking between levels) and that we can put them into a broader context. We further model five different abstraction concepts from the planning literature. Analysing what method properties they have highlights their fundamental differences and similarities. Finally, we prove that method properties sometimes imply instance properties. Taking also those instance properties into account reveals important information about computational aspects of the five methods.

In this paper we encode some of the reasoning methods used in frame based knowledge representation languages in answer set programming (ASP). In particular, we show how ``cloning'' and ``unification'' in frame based systems can be encoded in ASP. We then show how some of the types of queries with respect to a biological knowledge base can be encoded using our methodology. We also provide insight on how the reasoning can be done more efficiently when dealing with a huge knowledge base.

Given a 4-tuple of Boolean variables (a, b, c, d), logical proportions are modeled by a pair of equivalences relating similarity indicators (a ∧ b and a ∧ b), or dissimilarity indicators (a ∧ b and a ∧ b) pertaining to the pair (a, b), to the ones associated with the pair (c, d). Logical proportions are homogeneous when they are based on equivalences between indicators of the same kind. There are only 4 such homogeneous proportions, which respectively express that i) “a differs from b as c differs from d” (and “b differs from a as d differs from c”), ii) “a differs from b as d differs from c” (and “b differs from a as c differs from d”), iii) “what a and b have in common c and d have it also”, iv) “what a and b have in common neither c nor d have it”. We prove that each of these proportions is the unique Boolean formula (up to equivalence) that satisfies groups of remarkable properties including a stability property w.r.t. a specific permutation of the terms of the proportion. The first one (i) is shown to be the only one to satisfy the standard postulates of an analogical proportion. The paper also studies how two analogical proportions can be combined into a new one. We then examine how homogeneous proportions can be used for diverse prediction tasks. We particularly focus on the completion of analogical-like series, and on missing value abduction problems. Finally, the paper compares our approach with other existing works on qualitative prediction based on ideas of betweenness, or of matrix abduction.

Many examples of epistemic reasoning in the literature exhibit a stratified structure: defaults are formulated on top of an incomplete knowledge base. These defaults derive extra information in case information is missing in the knowledge base. In autoepistemic logic, default logic and ASP this inherent stratification is not preserved as they may refer to their own knowledge or logical consequences. Defining the semantics of such logics requires a complex mathematical construction. As an alternative, this paper further develops ordered epistemic logic. This logic extends first order logic with a modal operator and stratification is maintained. This allows us to define an easy to understand semantics. Moreover, inference tasks have a lower complexity than in autoepistemic logic and the logic integrates seamlessly into classical logic and its extensions. In this paper we also propose a generalization of ordered epistemic logic, which we call distributed ordered epistemic logic. We argue that it can provide a semantic foundation for a number of distributed knowledge representation formalisms found in the literature.

We consider the setting of ontological database access, where an A-box is given in form of a relational database D and where a Boolean conjunctive query q has to be evaluated against D modulo a T-box Σ formulated in DL-Lite or Linear Datalog+/-. It is well-known that (Σ, q) can be rewritten into an equivalent nonrecursive Datalog program P that can be directly evaluated over D. However, for Linear Datalog+/- or for DL-Lite versions that allow for role inclusion, the rewriting methods described so far result in a nonrecursive Datalog program P of size exponential in the joint size of Σ and q . This gives rise to the interesting question whether such a rewriting necessarily needs to be of exponential size. In this paper we show that it is actually possible to translate (Σ, q ) into a polynomially sized equivalent nonrecursive Datalog program P.

Answering queries in information systems that allow for ex- pressive inferencing is currently a field of intense research. This problem is often referred to as ontology-based data ac- cess (OBDA). We focus on conjunctive query entailment un- der logical rules known as tuple-generating dependencies, existential rules or Datalog+/-. One of the most expressive decidable classes of existential rules known today is that of greedy bounded treewidth sets (gbts). We propose an algo- rithm for this class, which is worst-case optimal for data and combined complexities, with or without bound on the pred- icate arity. A beneficial feature of this algorithm is that it allows for separation between offline and online processing steps: the knowledge base can be compiled independently from queries, which are evaluated against the compiled form. Moreover, very simple adaptations of the algorithm lead to worst-case-optimal complexities for specific subclasses of gbts which have lower complexities, such as guarded rules.

Datalog ∃ is the extension of Datalog, allowing existentially quantified variables in rule heads. This language is highly expressive and enables easy and powerful knowledge-modeling, but the presence of existentially quantified variables makes reasoning over Datalog^E undecidable, in the general case. The results in this paper enable powerful, yet decidable and efficient reasoning (query answering) on top of Datalog ∃ programs. On the theoretical side, we define the class of parsimonious Datalog ∃ programs, and show that it allows of decidable and efficiently-computable reasoning. Unfortunately, we can demonstrate that recognizing parsimony is undecidable. However, we single out Shy, an easily recognizable fragment of parsimonious programs, that significantly extends both Datalog and Linear-Datalog ∃ , while preserving the same (data and combined) complexity of query answering over Datalog, although the addition of existential quantifiers. On the practical side, we implement a bottom-up evaluation strategy for Shy programs inside the DLV system, enhancing the computation by a number of optimization techniques to result in DLV ∃ — a powerful system for answering conjunctive queries over Shy programs, which is profitably applicable to ontology-based query answering. Moreover, we carry out an experimental analysis, comparing DLV ∃ against a number of state-of-the-art systems for ontology-based query answering. The results confirm the effectiveness of DLV ∃ , which outperforms all other systems in the benchmark domain.

McCarthy's strengths as both theoretician and engineer, John McCarthy, famous for his role in the development and explore how these drosophilae shaped his of time-sharing, for inventing the computer research. Since is talk analyzes McCarthy's myriad contributions 2010, she has served as principal investigator of the to artificial intelligence and knowledge representation Evaluation and Knowledge Infrastructure Team for through the set of drosophilae that he proposed, DARPA's Machine Reading Program.

In this work we use Branch-and-Bound (BB) to efficiently detect objects with deformable part models. Instead of evaluating the classifier score exhaustively over image locations and scales, we use BB to focus on promising image locations. The core problem is to compute bounds that accommodate part deformations; for this we adapt the Dual Trees data structure to our problem. We evaluate our approach using Mixture-of-Deformable Part Models. We obtain exactly the same results but are 10-20 times faster on average. We also develop a multiple-object detection variation of the system, where hypotheses for 20 categories are inserted in a common priority queue. For the problem of finding the strongest category in an image this results in up to a 100-fold speedup.