André Trudel

Time-BPMN

We present Time-BPMN which is an extension to the current version of the Business Process Modeling Notation (BPMN). Time-BPMN captures the temporal perspective of business processes. This extension deals with the various temporal constraints and dependencies that may occur while characterizing real world business processes. Time-BPMN allows for the specification and depiction of temporal constraints and dependencies within a BPMN diagram. It not only extends, but also simplifies certain existing temporal BPMN constructs. With our extension, BPMN becomes expressive enough to account for a very large set of real world business processes that are time dependent.

Probabilistic temporal interval networks

A probabilistic temporal interval network is a constraint satisfaction problem where the nodes are temporal intervals and the edges are uncertain interval relations. We attach a probability to each of Allens basic interval relations. An uncertain relation between two temporal intervals is represented as a disjunction of Allens probabilistic basic relations. Using the operations of inversion, composition, and addition, defined for this probabilistic representation, we present a path consistency algorithm.

World's first web census

Purpose – To measure the exact size of the world wide web (i.e. a census). The measure used is the number of publicly accessible web servers on port 80.Design/methodology/approach – Every IP address on the internet is queried for the presence of a web server.Findings – The census found 18,560,257 web servers.Research limitations/implications – Any web servers hidden behind a firewall, or that did not respond within a reasonable amount of time (20 seconds) were not counted by the census.Practical implications – Whenever a server is found, we download and store a copy of its homepage. The resulting database of homepages is a historical snapshot of the web which will be mined for information in the future.Originality/value – Past web surveys performed by various research groups were only estimates of the size of the web. This is the first time its size has been exactly measured.

A Highly Flexible Student Driven Architecture for Computer Based Instruction

We propose a domain independent framework for computer based instruction systems capable of generating custom courses. A salient property of our framework is that the corpus content, pedagogical course structure, and course delivery are independent. The system does not use a student model. We believe that the student is the best model of himself and provide him with the power to dynamically change the courses delivery.

Interval Algebra Networks with Infinite Intervals

Interval algebra networks are traditionally defined over finite intervals. In this paper, we relax this restriction by allowing one or more of the intervals involved to be infinite. We then show how algorithms developed for solving interval algebra networks with finite intervals can be used, with minor modifications, in the infinite case.

A Multi-Agent Temporal Constraint Satisfaction System Based on Allen's Interval Algebra and Probabilities

We present a multi-agent distributed temporal scheduling system based on probabilistic Interval Algebra networks (i.e., PIA networks). The overall goal is to make each agent’s network consistent and optimal. Each agent is independent and has sole control over its network. But, it must communicate and coordinate with other agents when modifying or updating shared edges. We present an algorithm which allows the agents to collaboratively solve and recommend a temporal schedule. At the agent level, this schedule is optimal under the given local constraints. Although the global solution may not be optimal, we try to generate near optimal ones. Note that our distributed system is not centrally controlled. We use our system to solve scheduling problems from a university problem domain. We also describe the implementation of our proposed system which uses Java, JXTA, and JGraph.

Efficient Heuristics for Solving Probabilistic Interval Algebra Networks

A probabilistic interval algebra (PIA) network is an interval algebra network with probabilities associated with the labels on an edge. The probabilities on each edge sum to 1. A solution is a consistent scenario where the product of the probabilities associated with each unique edge label is maximized. In this paper we investigate previous PIA network solution algorithms, and propose new ones. Our first algorithm is based on best first search and guarantees to output the optimal solution. However, this algorithm is only feasible for toy problems. We augment the algorithm with three heuristics. Although our proposed algorithm does not guarantee an optimal solution, it is very useful in practice. Good solutions can be generated quickly

A spatio-temporal logic for 2D multi-agent problem domains

Abstract We formally present a first-order logic intended for representing and reasoning about 2D dynamic multi-agent problem domains. The unique feature of the logic is the uniform use of a Cartesian plane as the basis for both the spatial and temporal ontology. Our temporal structure has an ever changing present. Relative to each present there is a past and a future. A feature of this temporal structure is its ability to capture when knowledge is added or updated.

Probabilistic Regions of Persistence

Perhaps the most difficult, and certainly the most intensely studied problem in temporal reasoning is the persistence of information-that is, what reasonable inferences can we draw about non-change given partial knowledge of the world and of the changes taking place. Almost all previous work hinges on McCarthys common sense law of inertia (CSLI): things tend not to change. The obvious consequence of adopting this view is that it becomes reasonable to infer that the duration of non-change is arbitrarily long. For instance, a typical inference in systems that appeal to CSLI is that if a person is alive now, the person will remain alive (arbitrarily long) until something happens that results in the persons death.

Representing Allen's properties, events, and processes

Allens logic is one of the most influential temporal logics in AI. In his logic, he defines properties, events, and processes. Properties and events are commonly used in other logics. Processes are not so popular and rarely appear in other logics. Ignoring processes in a logic means that a large portion of temporal information cannot be represented.We present a first order temporal logic that can represent properties, events and processes. We use a standard representation for properties and events. The distinguishing feature of the logic is the representation of processes in terms of what is true at the point level. We provide axiom schemas for representing processes.