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N E GAT I V E C ONSE QU E NC E S O F C OU P LE S P H YS ICA L AG GR E SS ION Although the prevalence rates of male and female physical aggression against an intimate partner are comparable, the consequences of partner violence differ for men and women (Archer, 2000) Women suffer more fear and physical injury In a nationally representative sample, Stets and Straus (1989) found that 3% of women reported having needed medical attention as a result of marital violence, compared with only 04% of men Among couples seeking marital therapy, Cascardi and colleagues (1992) found that, relative to husbands, wives rated the impact of marital violence as more negative and were more likely to sustain serious injuries Women are also more likely to report experiencing fear in response to partner violence (eg.

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, Cantos, Neidig, & O Leary, 1993) Relative to husbands, wives are also more likely to experience negative psychological consequences as a result of partner physical aggression For example, studies of couples seeking marital therapy demonstrate that wives in violent marriages are at risk for depression (eg, Christian, O Leary, & Vivian, 1994) Quigley and Leonard (1996) found that newlywed wives experiencing husband physical aggression had higher levels of depressive symptomatology in the first three years of marriage than wives whose husbands had ceased physical aggression or wives in nonviolent marriages Research findings similarly suggest that children who reside in maritally violent households are at risk for internalizing and externalizing problems and impaired social and academic functioning Again, however, the data suggest that fewer differences are found between children in violent and nonviolent homes when community samples are studied (ie.

, likely to be lower levels of violence) than when children from nonviolent homes are compared to children in shelters or clinical samples (see review by Margolin, 1998) Marital distress and dissolution are other consequences of marital aggression O Leary and colleagues (1989) found that among newlyweds, the.

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20 times a second. Our cardiologists (busy as most doctors are) wanted to have a computer look at digitized echocardiograms to determine the volume changes throughout the heart cycle. We will look here at a somewhat simpli ed version of the expert system for classifying regions of the image according to their anatomical signi cance (Siler et al., 1987). In classical image processing of this nature, there are usually three steps: segmentation, feature extraction, and classi cation. We decided to perform the rst two by conventional procedural language programs. First, we mapped out regions of interest in the heart image. Next, we extracted numerical features of these regions: area, x- and y-centroid coordinates, and measures of shape (not needed in the nal classi cation program). Finally, we planned a fuzzy expert system to classify the regions. FLOPS was originally written as a fuzzy superset of an AI-based non-fuzzy expert system shell written by Charles Forgy of Carnegie-Mellon University, OPS5 (Brownston et al., 1985). The segmentation program was based on using a cell-automation algorithm that worked quite successfully. Region feature extraction, into area; centroid position; and whether the region touched the border of each region, was a straightforward task. Now came the FLOPS region classi cation program. 13.3.1 Echocardiogram Pattern Recognition: Data Declarations

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bulk of existing multi-robot systems along various requirements are presented. Those requirements include team organization (e.g. centralized vs. distributed), communication topology (e.g. broadcast vs. unicast), and team composition (e.g. homogenous vs. heterogenous). 3.6.2. A taxonomy of multi-robot systems A key dif culty in the design of multi-robot systems is the size and complexity of the space of possible designs. In order to make principled design decisions, an understanding of the many possible system con gurations is essential. Several taxonomies have been proposed which provide the following aspects: de ning key features that help identify different multi-robot systems in a precise and complete way; making it possible to state relations, analysis and formal proofs for the groups derived from the taxonomy; making it easier to compare different systems in a common and simple framework; describing the extent of the space of possible designs for a multi-robot system; in this way, during the development of a new system, it is possible to identify problems previously solved.

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