There is interest in artificial intelligence for principled techniques to analyze inconsistent information. This stems from the recognition that the dichotomy between consistent and inconsistent sets of formulae that comes from classical logics is not sufficient for describing inconsistent information. We review some existing proposals and make new proposals for measures of inconsistency and measures of information, and then prove that they are all pairwise incompatible. This shows that the notion of inconsistency is a multi-dimensional concept where different measures provide different insights. We then explore relationships between measures of inconsistency and measures of information in terms of the trade-offs they identify when using them to guide resolution of inconsistency.

We show how Finite Model Computation (FMC) of first-order theories can efficiently and transparentlybe solved by taking advantage of an extension of Answer Set Programming, called incremental Answer Set Programming (iASP). The idea is to use the incremental parameter in iASP programs to account for the domain size of a model. The FMC problem is then successively addressed for increasing domain sizes until an answer set, representing a finite model of the original first-order theory, is found. We developed a system based on the iASP solver iClingo and demonstrate its competitiveness.

There is a huge and growing number of systems that depend on batteries for power supply, ranging from small mobile devices to large high-powered systems such as electrical substations. In most of these systems, there are significant user-benefits or engineering reasons to base the supply on multiple batteries, with load being switched between batteries by a control system. The key to efficient use of multiple batteries lies in the design of effective policies for the management of the switching of load between them. This paper describes work in which we show that automated planning can produce much more effective policies than other approaches to multiple battery load management in the literature.

In this paper we present a novel user interface that integrates two popular approaches to language translation for travelers allowing multimodal communication between the parties involved: the picture-book, in which the user simply points to multiple picture icons representing what they want to say, and the statistical machine translation system that can translate arbitrary word sequences. Our prototype system tightly couples both processes within a translation framework that inherits many of the the positive features of both approaches, while at the same time mitigating their main weaknesses. Our system differs from traditional approaches in that its mode of input is a sequence of pictures, rather than text or speech. Text in the source language is generated automatically, and is used as a detailed representation of the intended meaning. The picture sequence which not only provides a rapid method to communicate basic concepts but also gives a `second opinion' on the machine transition output that catches machine translation errors and allows the users to retry the translation, avoiding misunderstandings.

Consider a combinatorial state space S, such as the set of all truth assignments to N Boolean variables. Given a partition of S, we consider the problem of estimating the size of all the subsets in which S is divided. This problem, also known as computing the density of states, is quite general and has many applications. For instance, if we consider a Boolean formula in CNF and we partition according to the number of violated constraints, computing the density of states is a generalization of both SAT, MAXSAT and model counting. We propose a novel Markov Chain Monte Carlo algorithm to compute the density of states of Boolean formulas that is based on a flat histogram approach. Our method represents a new approach to a variety of inference, learning, and counting problems. We demonstrate its practical effectiveness by showing that the method converges quickly to an accurate solution on a range of synthetic and real-world instances.

We investigate the problem of influencing the preferences of players within a Boolean game so that, if all players act rationally, certain desirable outcomes will result. The way in which we influence preferences is by overlaying games with taxation schemes. In a Boolean game, each player has unique control of a set of Boolean variables, and the choices available to the player correspond to the possible assignments that may be made to these variables. Each player also has a goal, represented by a Boolean formula, that they desire to see satisfied. Whether or not a player’s goal is satisfied will depend both on their own choices and on the choices of others, which gives Boolean games their strategic charac- ter. We extend this basic framework by introducing an external principal who is able to levy a taxation scheme on the game, which imposes a cost on every possible action that a player can choose. By designing a taxation scheme appropriately, it is possible to perturb the preferences of the players, so that they are incentivised to choose some equilibrium that would not otherwise be chosen. After motivating and formally presenting our model, we explore some issues surrounding it, including the complexity of finding a taxation scheme that implements some socially desirable outcome, and then discuss desirable properties of taxation schemes.

We solve constraint satisfaction problems through translation to answer set programming (ASP). Our reformulations have the property that unit-propagation in the ASP solver achieves well defined local consistency properties like arc, bound and range consistency. Experiments demonstrate the computational value of this approach.

The paper investigates a novel approach, based on Constraint Logic Programming (CLP), to predict potential 3D conformations of a protein via fragments assembly. The fragments are extracted and clustered by a preprocessor from a database of known protein structures. Assembling fragments into a complete conformation is modeled as a constraint satisfaction problem solved using CLP. The approach makes use of a simplified CA-side chain centroid protein model, that offers efficiency and a good approximation for space filling. The approach adapts existing energy models for protein representation and applies a large neighboring search (LNS) strategy. The results show the feasibility and efficiency of the method, and the declarative nature of the approach simplifies the introduction of additional knowledge and variations of the model.

This paper presents an algorithm of adaptation for a case-based reasoning system with cases and domain knowledge represented in the expressive description logic ALC. The principle is to first pretend that the source case to be adapted solves the current target case. This may raise some contradictions with the specification of the target case and with the domain knowledge. The adaptation consists then in repairing these contradictions. This adaptation algorithm is based on an extension of the classical tableau method used for deductive inferences in ALC.

We introduce a game-theoretic framework to address the community detection problem based on the social networks’ structure. The dynamics of community formation is framed as a strategic game called community formation game: Given a social network, each node is selfish and selects communities to join or leave based on her own utility measurement. A community structure can be interpreted as an equilibrium of this game. We formulate the agents’ utility by the combination of a gain function and a loss function. Each agent can select multiple communities, which naturally captures the concept of “overlapping communities”. We propose a gain function based on Newman’s modularity function and a simple loss function that reflects the intrinsic costs incurred when people join the communities. We conduct extensive experiments under this framework; our results show that our algorithm is effective in identifying overlapping communities, and is often better than other algorithms we evaluated especially when many people belong to multiple communities.