The paper describes a method of relation extraction, which is based on parsing the input text using a combination of a generic HPSG-based grammar and a highly focused domain- and relation-specific lexicon. We also show a method of unsupervised acquisition of such a lexicon from a large unlabeled corpus. Together, the methods introduce a novel approach to the “Open IE” task, which is superior in accuracy and in quality of relation identification to the existing approaches.

The paper describes a method of relation extraction, which is based on parsing the input text using a combination of a generic HPSG-based grammar and a highly focused domain- and relation-specific lexicon. We also show a method of unsupervised acquisition of such a lexicon from a large unlabeled corpus. Together, the methods introduce a novel approach to the “Open IE” task, which is superior in accuracy and in quality of relation identification to the existing approaches.

The paper describes a method of relation extraction, which is based on parsing the input text using a combination of a generic HPSG-based grammar and a highly focused domain- and relation-specific lexicon. We also show a method of unsupervised acquisition of such a lexicon from a large unlabeled corpus. Together, the methods introduce a novel approach to the “Open IE” task, which is superior in accuracy and in quality of relation identification to the existing approaches.

The paper describes a method of relation extraction, which is based on parsing the input text using a combination of a generic HPSG-based grammar and a highly focused domain- and relation-specific lexicon. We also show a method of unsupervised acquisition of such a lexicon from a large unlabeled corpus. Together, the methods introduce a novel approach to the “Open IE” task, which is superior in accuracy and in quality of relation identification to the existing approaches.

The paper describes a method of relation extraction, which is based on parsing the input text using a combination of a generic HPSG-based grammar and a highly focused domain- and relation-specific lexicon. We also show a method of unsupervised acquisition of such a lexicon from a large unlabeled corpus. Together, the methods introduce a novel approach to the “Open IE” task, which is superior in accuracy and in quality of relation identification to the existing approaches.

An online double auction mechanism for dynamic environments, especially dynamic has to match sellers and buyers dynamically and calculate double auctions. After a brief review of related a payment for each matched trader without knowing work, we specify the problem we are tackling, and about future orders. Such uncertainty is more challenging for then briefly outline our research plan, the results we double auction mechanism design because modelling traders' have achieved to date, and the ongoing directions.

Multi-agent path planning is a challenging problem with numerous real-life applications, including robotics, logistics, military operations planning, disaster rescue, and computer games. We look at navigating large numbers of mobile units to their targets on navigation graphs such as grid maps. The size of problems examined is significantly larger than can be handled using optimal multi-agent pathfinding algorithms in practice. We introduced MAPP, a tractable algorithm for multi-agent path planning on undirected graphs. MAPP and its extended versions are complete on well specified and tractably testable classes of problems. They have low-polynomial worst-case upper bounds for the running time, the memory requirements, and the length of solutions. Experiments on realistic game grid maps, with uniformly randomly generated start and target locations for each unit, show MAPP as a state-of-the-art multi-agent pathfinding algorithm in terms of scalability and success ratio (i.e., percentage of solved units). Even on challenging scenarios with 2000 units, MAPP solves 92% to 99.7% of units. FAR and WHCA*, two fast but incomplete algorithms that were previously state-of-the-art in terms of scalability, solve as few as 17.5% and 12.3% of these problems. The quality of MAPP's solutions is empirically analyzed using multiple quality criteria: total travel distance, makespan, and sum of actions (including move and wait actions). MAPP is competitive in terms of solution quality and speed with FAR and WHCA*. MAPP further provides the formal characterizations that FAR and WHCA* lack, on problems it can solve as well as low-polynomial upper bounds on the resources required. As optimal algorithms have limited scalability, we evaluated the solution quality of suboptimal algorithms using lower bounds of optimal values. We showed that MAPP's solutions have a reasonable quality. For example, MAPP's total travel distance is on average 19% longer than a lower bound on the optimal value.

Behaviour recognition aims to infer the particular behaviours of the inhabitant in a smart home from a series of sensor readings from around the house. There are many reasons to recognise human behaviours; one being to monitor the elderly or cognitively impaired and detect potentially dangerous behaviours. We view the behaviour recognition problem as the task of mapping the sensory outputs to a sequence of recognised activities. This research focuses on the development of machine learning methods to find an approximation to the mapping between sensor outputs and behaviours. However, learning the mapping raises an important issue, which is that the training data is not necessarily annotated with exemplar behaviours of the inhabitant. This doctoral study takes several steps towards addressing the problem of finding an approximation to this mapping, beginning with separate investigations on current methods proposed in the literature, identifying useful sensory outputs for behaviour recognition, and concluding by proposing two directions: one using supervised learning on annotated sensory stream and one using unsupervised learning on unannotated ones.

The Semantic Web aims to offer an interoperable environment that will allow users to safely delegate complex actions to intelligent agents. Much work has been done for agents' interoperability; especially in the areas of ontology-based metadata and rule-based reasoning. Nevertheless, the SW proof layer has been neglected so far, although it is vital for agents and humans to understand how a result came about, in order to increase the trust in the interchanged information. This paper focuses on the implementation of third party SW reasoning and proofing services wrapped as agents in a multi-agent framework. This way, agents can exchange and justify their arguments without the need to conform to a common rule paradigm. Via external reasoning and proofing services, the receiving agent can grasp the semantics of the received rule set and check the validity of the inferred results.

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.