Irène Abi-Zeid

SARPlan: A decision support system for Canadian Search and Rescue Operations

We present SARPlan, a geographic decision support system designed to assist the Canadian Forces in the optimal planning of search missions for missing aircraft. Its primary purpose is to ensure that the available search resources are deployed in a way that will maximize the missions probability of success. The optimization modules are based on search theory, on gradient search methods and on constraint satisfaction programming. We include results that demonstrate that SARPlan improves the performance when compared to the current manual method. This improvement translates to an increase in the chances of finding lost aircraft and survivors, resulting in more saved lives. Another benefit of using SARPlan is a potential decrease in the operations costs. In 2001, SARPlan was the winner of three prestigious excellence awards in the information technology domain.

Value-based argumentation for policy decision analysis: methodology and an exploratory case study of a hydroelectric project in Québec

In many countries, development projects that may have a substantial impact on the environment are submitted to a public evaluation process within which citizens use argumentation to express and justify their positions regarding a project. These justifications typically refer to various values. Subsequently, a public commission in charge of the evaluation process arrives at a conclusion. But how are the arguments of the various participants taken into account? How do values influence the commission’s recommendation? In order to arrive to an understanding of a commission’s decision process, we focus on the argumentative nature of the process and apply a methodology combining content analysis and a value-based argumentative framework. This methodology was illustrated using a case study of a hydroelectric project in Québec. First, we analysed a corpus of unstructured texts produced during public hearings and extracted the arguments and values of the participants. We then used a computational model to obtain the commission’s possible hypothetical decisions which we compared with the commission’s actual conclusion. Furthermore, we identified some preference elements of the commission, and we partially explained their attitude towards conflicting and incoherent arguments. Finally, based on our experience, we formulated some conclusions regarding the ability and promise of argumentative methods to support decision making in a participatory context.

Constraint programming for path planning with uncertainty: solving the optimal search path problem

The optimal search path (OSP) problem is a single-sided detection search problem where the location and the detectability of a moving object are uncertain. A solution to this

Is CBR Applicable to the Coordination of Search and Rescue Operations? A Feasibility Study

\mathcal{NP}

Multicriteria decision analysis applied to the design of light-frame wood wall assemblies

-hard problem is a path on a graph that maximizes the probability of finding an object that moves according to a known motion model. We developed constraint programming models to solve this probabilistic path planning problem for a single indivisible searcher. These models include a simple but powerful branching heuristic as well as strong filtering constraints. We present our experimentation and compare our results with existing results in the literature. The OSP problem is particularly interesting in that it generalizes to various probabilistic search problems such as intruder detection, malicious code identification, search and rescue, and surveillance.

Combining multiple similarity metrics using a multicriteria approach

In response to the occurrence of an air incident, controllers at one of the three Canadian Rescue Coordination Centers (RCC) must make a series of critical decisions on the appropriate procedures to follow. These procedures (called incident prosecution) include hypotheses formulation and information gathering, development of a plan for the search and rescue (SAR) missions and in the end, the generation of reports. We present in this paper the results of a project aimed at evaluating the applicability of CBR to help support incident prosecution in the RCC. We have identified three possible applications of CBR: Online help, real time support for situation assessment, and report generation. We present a brief description of the situation assessment agent system that we are implementing as a result of this study.

A hybrid algorithm for coverage path planning with imperfect sensors

Designers of light-frame wood wall assemblies should consider several performance criteria simultaneously in addition to minimum requirements set by building codes. Evaluation of various performance characteristics can help select the most suitable configuration for a given project in a given context. In a previous article, we identified appropriate criteria and explored their means of evaluation. Following up, this article integrates the context and the preference of the decision-maker and seeks to obtain a ranking of the various alternatives using quantitative and qualitative information. Four multicriteria decision analysis (MCDA) techniques are explored: weighted sum, MACBETH, ELECTRE II, and PROMETHEE. The approach is applied to a case study where five wall assemblies are compared within six different decision contexts. It shows how the choice of the appropriate aggregation method depends on the nature of the performance evaluation scales and demonstrates the usefulness of MCDA to consider simultaneously different aspects of wall performance.

An agent system for intelligent situation assessment

The design of a CBR system involves the use of similarity metrics. For many applications, various functions can be adopted to compare case features and to aggregate them into a global similarity measure. Given the availability of multiple similarity metrics, the designer is hence left with two options in order to come up with a working system: Either select one similarity metric or try to combine multiple metrics in a super-metric. In this paper, we study how techniques borrowed from multicriteria decision aid can be applied to CBR for combining the results of multiple similarity metrics. The problem of multi-metrics retrieval is presented as an instance of the problem of ranking alternatives based on multiple attributes. Discrete methods such as ELECTRE II have been proposed by the multicriteria decision aid community to address such situations. We conducted our experiments for ranking cases with ELECTRE II, a procedure based on pairwise comparisons. We used textual cases and multiple metrics. Our results indicate that the use of a combination of metrics with a multicriteria decision aid method can increase retrieval precision and provide an advantage over weighted sum combinations especially when similarity is measured on scales that are different in nature.

A Constraint Optimization Approach for the Allocation of Multiple Search Units in Search and Rescue Operations

We are interested in the coverage path planning problem with imperfect sensors, within the context of robotics for mine countermeasures. In the studied problem, an autonomous underwater vehicle (AUV) equipped with sonar surveys the bottom of the ocean searching for mines. We use a cellular decomposition to represent the ocean floor by a grid of uniform square cells. The robot scans a fixed number of cells sideways with a varying probability of detection as a function of distance and of seabed type. The goal is to plan a path that achieves the minimal required coverage in each cell while minimizing the total traveled distance and the total number of turns. We propose an off-line hybrid algorithm based on dynamic programming and on a traveling salesman problem reduction. We present experimental results and show that our algorithms performance is superior to published results in terms of path quality and computational time, which makes it possible to implement the algorithm in an AUV.

Using a geographic decision support system to plan search and rescue operations

Coordinating Search and Rescue (SAR) operations is a knowledge and information intensive task. Upon receiving an initial indication about a possible aircraft related problem, a Rescue Coordination Center (RCC) Controller sets out to find out more about the nature of the problem. This situation assessment phase is highly complex due to the diverse and sophisticated nature of the many information sources. In this paper, we present an intelligent agent architecture incorporating multiple, continual information planning agents for assisting the RCC controller in performing critical situation assessment tasks. The agents monitor the actions of the human controller and make their decisions on when and how to acquire more information to help the human controller, and to remind him of important steps that may have been overlooked. The system is designed based on several technologies, including hierarchical task networks, case based retrieval and intelligent agent systems.