Understanding temporal and causal relations between events is a fundamental natural language understanding task. Because a cause must be before its effect in time, temporal and causal relations are closely related and one relation even dictates the other one in many cases. However, limited attention has been paid to studying these two relations jointly. This paper presents a joint inference framework for them using constrained conditional models (CCMs). Specifically, we formulate the joint problem as an integer linear programming (ILP) problem, enforcing constraints inherently in the nature of time and causality. We show that the joint inference framework results in statistically significant improvement in the extraction of both temporal and causal relations from text.

Coalition formation is a fundamental approach to multi-agent coordination. In this paper we address the specific problem of coalition structure generation, and focus on providing good-enough solutions using a novel heuristic approach that is based on data clustering methods. In particular, we propose a hierarchical agglomerative clustering approach (C-Link), which uses a similarity criterion between coalitions based on the gain that the system achieves if two coalitions merge. We empirically evaluate C-Link on a synthetic benchmark data-set as well as in collective energy purchasing settings. Our results show that the C-link approach performs very well against an optimal benchmark based on Mixed-Integer Programming, achieving solutions which are in the worst case about 80% of the optimal (in the synthetic data-set), and 98% of the optimal (in the energy data-set). Thus we show that C-Link can return solutions for problems involving thousands of agents within minutes.

When searching a knowledge repository such as Wikipedia or the Internet, the user doesn’t always know what they are looking for. Indeed, it is often the case that a user wishes to find information about a concept that was completely unknown to them prior to the search. In this paper we describe C-Link, which provides the user with a method for searching for unknown concepts which lie between two known concepts. C-Link does this by modeling the knowledge repository as a weighted, directed graph where nodes are concepts and arc weights give the degree of “relatedness” between concepts. An experimental study was undertaken with 59 participants to investigate the performance of C-Link compared to standard search approaches. Statistical analysis of the results shows great potential for C-Link as a search tool.