Symmetry is an important problem in many combinatorial problems. One way of dealing with symmetry is to add constraints that eliminate symmetric solutions. We survey recent results in this area, focusing especially on two common and useful cases: symmetry breaking constraints for row and column symmetry, and symmetry breaking constraints for eliminating value symmetry.

General Game Playing (GGP) agents must be capable of playing a wide variety of games skillfully. Monte-Carlo Tree Search (MCTS) has proven an effective reasoning mechanism for this challenge, as is reflected by its popularity among designers of GGP agents. Providing GGP agents with the knowledge relevant to the game at hand in real time is, however, a challenging task. In this paper we propose two enhancements for MCTS in the context of GGP, aimed at improving the effectiveness of the simulations in real time based on in-game statistical feedback. The first extension allows early termination of lengthy and uninformative simulations while the second improves the action-selection strategy when both explored and unexplored actions are available. The methods are empirically evaluated in a state-of-the-art GGP agent and shown to yield an overall significant improvement in playing strength.

A General Similarity Measurement (GSM), which takes into account of both non-temporal and rich temporal aspects including temporal order, temporal duration and temporal gap, is proposed for state-sequence matching. It is believed to be versatile enough to subsume representative existing measurements as its special cases.

In conservation biology and natural resource management, adaptive management is an iterative process of improving management by reducing uncertainty via monitoring. Adaptive management is the principal tool for conserving endangered species under global change, yet adaptive management problems suffer from a poor suite of solution methods. The common approach used to solve an adaptive management problem is to assume the system state is known and the system dynamics can be one of a set of pre-defined models. The solution method used is unsatisfactory, employing value iteration on a discretized belief MDP which restricts the study to very small problems. We show how to overcome this limitation by modelling an adaptive management problem as a restricted Mixed Observability MDP called hidden model MDP (hmMDP). We demonstrate how to simplify the value function, the backup operator and the belief update computation. We show that, although a simplified case of POMDPs, hm-MDPs are PSPACE-complete in the finite-horizon case. We illustrate the use of this model to manage a population of the threatened Gouldian finch, a bird species endemic to Northern Australia. Our simple modelling approach is an important step towards efficient algorithms for solving adaptive management problems.

Prior work done in learning by instruction (Huffman and Laird, 1995) Learning by interacting with humans is a powerful learning demonstrated learning systems that focus on agent-initiated paradigm. In a complex world learning through self-directed interaction, where instruction is directed by impasses arising experience alone can be slow, requiring repeated interactions in a Soar agent. They noted that instructor-initiated interaction with the environment. Learning from human-agent interaction is difficult to support because of the likely interruption can reduce the complexity of the learning task by reducing of agent's reasoning.

Metaphors being at the heart of our language and thought process, computationally modelling them is imperative for reproducing human cognitive abilities. In this work, we propose a plausible grounded cognitive model for artificial metaphor acquisition. We put forward a rule-based metaphor acquisition system, which doesn't make use of any prior 'seed metaphor set'. Through correlation between a video and co-occurring commentaries, we show that these rules can be automatically acquired by an early learner capable of manipulating multi-modal sensory input. From these grounded linguistic concepts, we derive classes based on lexico-syntactical language properties. Based on the selectional preferences of these linguistic elements, metaphorical mappings between source and target domains are acquired.

Efforts by the pervasive, context-aware system development community have over the years produced a wide variety of context-aware techniques and frameworks. However, a bulk of this technology tends to be strictly tied to a native system, thus largely limiting its external adoption. In addressing this limitation, we introduce an interoperable context representation format, in the form of an ontology, which models core context-aware concepts for re-use within pervasive computing environments. The DCON Context Ontology is proposed as a novel vocabulary for the representation of activity context as experienced by a user, and sensed through one or more of their devices. We demonstrate how, combined with other domain ontologies, DCON provides for richer representations of multi-level context interpretations that are integrated with other known background information about a user.

Recognizing social interests plays an important role of aiding human-computer interaction and human collaborative works. The recognition of social interest could be of great help to determine the smoothness of the interaction, which could be an indicator for group work performance and relationship. From socio-psychological theories, social engagement is the observable form of inner social interest, and represented as patterns of turn-taking and speech emotion during a face-to-face conversation. With these two kinds of features, a multi-layer learning structure is proposed to model the continuous trend of engagement. The level of engagement is classified into “high” and “low” two levels according to human-annotated score. In the result of assessing two-level engagemet, the highest accuracy of our model can reach 79.1%.

Despite many years of research and progress in the field tecture, the testing environment in which the implementation of artificial intelligence, there is still no universally accepted will be embedded, and then describes the work completed so definition of the word intelligence. Finally we will address the body of work still to be completed identified a multitude of tasks, skills, and behaviours that and plans for future research. Much A.I research is focused Although the initial thrust multiplicity, heterogeneity, and adaptability. of A.I in the 1950s was towards this kind of integrated system, Multiplicity. One of the few points of consensus within in recent times the problem of integration has become cognitive architecture research is that architectures must be conspicuous by its absence in the field, but is essential to improve composed of modular, independent components. This is a our design of complete intelligent systems, and consequently consequence of the multifaceted nature of information processing, our understanding of our own brains.

Clustering ensemble mainly relies on the pairwise similarity to capture the consensus function. However, it usually considers each base clustering independently, and treats the similarity measure roughly with either 0 or 1. To address these two issues, we propose a coupled framework of clustering ensembles CCE, and exemplify it with the coupled version CCSPA for CSPA. Experiments demonstrate the superiority of CCSPA over baseline approaches in terms of the clustering accuracy.