We present a novel and highly effective approach for multi-body motion segmentation. Drawing inspiration from robust statistical model fitting, we estimate putative subspace hypotheses from the data. However, instead of ranking them we encapsulate the hypotheses in a novel Mercer kernel which elicits the potential of two point trajectories to have emerged from the same subspace. The kernel permits the application of well-established statistical learning methods for effective outlier rejection, automatic recovery of the number of motions and accurate segmentation of the point trajectories. The method operates well under severe outliers arising from spurious trajectories or mistracks. Detailed experiments on a recent benchmark dataset (Hopkins 155) show that our method is superior to other state-of-the-art approaches in terms of recovering the number of motions, segmentation accuracy, robustness against gross outliers and computational efficiency.

Secure networks rely upon players to maintain security and reliability. However not every player can be assumed to have total loyalty and one must use methods to uncover traitors in such networks. We use the original concept of the Byzantine Generals Problem by Lamport, and the more formal Byzantine Agreement describe by Linial, to nd traitors in secure networks. By applying general fault-tolerance methods to develop a more formal design of secure networks we are able to uncover traitors amongst a group of players. We also propose methods to integrate this system with insecure channels. This new resiliency can be applied to broadcast and peer-to-peer secure communication systems where agents may be traitors or become unreliable due to faults.

In Dempster-Shafer belief theory, general beliefs are expressed as belief mass distribution functions over frames of discernment. In Subjective Logic beliefs are expressed as belief mass distribution functions over binary frames of discernment. Belief representations in Subjective Logic, which are called opinions, also contain a base rate parameter which express the a priori belief in the absence of evidence. Philosophically, beliefs are quantitative representations of evidence as perceived by humans or by other intelligent agents. The basic operators of classical probability calculus, such as addition and multiplication, can be applied to opinions, thereby making belief calculus practical. Through the equivalence between opinions and Beta probability density functions, this also provides a calculus for Beta probability density functions. This article explains the basic elements of belief calculus.

Lexical constraints on the input of speech and on-line handwriting systems improve the performance of such systems. A significant gain in speed can be achieved by integrating in a digraph structure the different Hidden Markov Models (HMM) corresponding to the words of the relevant lexicon. This integration avoids redundant computations by sharing intermediate results between HMM's corresponding to different words of the lexicon. In this paper, we introduce a token passing method to perform simultaneously the computation of the a posteriori probabilities of all the words of the lexicon. The coding scheme that we introduce for the tokens is optimal in the information theory sense. The tokens use the minimum possible number of bits. Overall, we optimize simultaneously the execution speed and the memory requirement of the recognition systems.

This article considers the task of automatically inducing role-semantic annotations in the FrameNet paradigm for new languages. We propose a general framework that is based on annotation projection, phrased as a graph optimization problem. It is relatively inexpensive and has the potential to reduce the human effort involved in creating role-semantic resources. Within this framework, we present projection models that exploit lexical and syntactic information. We provide an experimental evaluation on an English-German parallel corpus which demonstrates the feasibility of inducing high-precision German semantic role annotation both for manually and automatically annotated English data.

A novel analytical methodology has proven fruitful in developing a functional identification of consciousness with operable mental control structure in human higher brain function. Two operational homologies (one associated with language, the other tool use) derived from mammalian instrumental behavioral competence are identified, each exadaptively accessible: one a specialization of attentive search to (conventional, linguistic) internalized symbolic lexicon; the second being a combination – a co-parallel activation – of symbolically specialized attention with the original external ‘spotlight’ in order to support (deliberative, choice-making) navigational tasking. The mechanism by which consciousness becomes articulated to support the specialized control requirements of three cognitive performance levels is described, in particular for the case of the social bipedal hominid. A single articulated template model is posed to intervene between the incoherent neuronal and the coherently conscious mental level of higher brain operation. This cognitive system theory logic lends itself to an explanation of the exadaptive acquisition of a cognitively objectifiable self-model from within subjective experience, and a plausible heuristic for the systematic building of self-aware mental repertoire is discovered.

We present a novel reasoning calculus for the description logic SHOIQ^+---a knowledge representation formalism with applications in areas such as the Semantic Web. Unnecessary nondeterminism and the construction of large models are two primary sources of inefficiency in the tableau-based reasoning calculi used in state-of-the-art reasoners. In order to reduce nondeterminism, we base our calculus on hypertableau and hyperresolution calculi, which we extend with a blocking condition to ensure termination. In order to reduce the size of the constructed models, we introduce anywhere pairwise blocking. We also present an improved nominal introduction rule that ensures termination in the presence of nominals, inverse roles, and number restrictions---a combination of DL constructs that has proven notoriously difficult to handle. Our implementation shows significant performance improvements over state-of-the-art reasoners on several well-known ontologies.

Heuristic forward search is the dominant approach to satisficing planning to date. Most successful planning systems, however, go beyond plain heuristic search by employing various search-enhancement techniques.  One example is the use of helpful actions or preferred operators, providing information which may complement heuristic values.  A second example is deferred heuristic evaluation, a search variant which can reduce the number of costly node evaluations. Despite the wide-spread use of these search-enhancement techniques however, we note that few results have been published examining their usefulness. In particular, while various ways of using, and possibly combining, these techniques are conceivable, no work to date has studied the performance of such variations.  In this paper, we address this gap by examining the use of preferred operators and deferred evaluation in a variety of settings within best-first search. In particular, our findings are consistent with and help explain the good performance of the winners of the satisficing tracks at IPC 2004 and 2008.

We provide a theoretical analysis of planning via Petri net unfolding, a novel technique for synthesising parallel plans. Parallel plans are generally valued for their execution flexi- bility, which manifests as alternative choices for the order- ing of operators and potentially faster plan executions. Being a relatively new approach, the flexibility properties of plans synthesised via unfolding, and even the concurrency seman- tics supported by this technique, are particularly unclear and only understood at an informal level. In this paper, we first formally characterise the concurrency semantics of planning via unfolding as a further restriction on the standard notion of independence. More importantly, we then prove that plans obtained using this approach are optimal deorderings and op- timal reorderings in terms of the number of ordering con- straints on operators and plan execution time, respectively. These results provide objective guarantees on the quality of plans obtained by the unfolding technique.

This paper presents multi-goal planning for an autonomous blasthole drill used in open pit mining operations. Given a blasthole pattern to be drilled and constraints on the vehicle's motion and orientation when drilling, we wish to compute the best order in which to drill the given pattern. Blasthole pattern drilling is an asymmetric Traveling Salesman Problem with precedence constraints specifying that some holes must be drilled before others. We wish to find the minimum cost tour according to criteria that minimize the distance travelled satisfying the precedence and vehicle motion constraints. We present an iterative method for solving the blasthole sequencing problem using the combination of a Genetic Algorithm and motion planning simulations that we use to determine the true cost of travel between any two holes.