Embedding spaces

Whereas we consider shape algebras U _ij specifying rectilinear spatial elements of dimension i within an (embedding – as opposed to visual –) space of dimension j, the specification of the dimension of the space is often omitted, assuming that the actual dimension, for example, 2D or 3D, is unimportant within the context or, otherwise, can be construed from the context. While there is little issue with the assumption itself, the implicit attention to the dimension of the space hides the fact that, irrespective of the dimension, shape algebras may operate in the same space or in different spaces. Jowers and Earl (Reference Jowers and Earl2015) recognize that a (basic) shape algebra is not only dependent on the representation of the shape, but also on the space and the set of transformations used. At a minimum, shape algebras operating in the same space compute under the same transformations, while shape algebras operating in different spaces may use different transformations. For example, in the case of a drawing of line segments and labeled points, the segments and points operate in the same (2D or 3D) Euclidean space and transformations of shapes of line segments and shapes of labeled points necessarily need to go hand in hand. Instead, when shape algebras operate in a different space (or drawing), transformations may differ.

Stiny (Reference Stiny1992) shows an example where a series of cubes is described separately in plan – presented in the algebra U ₁₂, in a front elevation with line weights decreasing as the edges of cubes recede – presented in the algebra W ₁₂, and in a side elevation as an arrangement of shaded areas that decrease in weight as the faces of cubes advance – in the algebra W ₂₂. For each drawing, the grammar considers three rules, an initialization rule, a rule to add a cube to the series – decreasing in size geometrically at a specified rate – and a finalization rule. Figure 3 shows Stiny's rule(s) adding a cube to the series in plan, front, and side elevation view. The three views embed points, line segments, and plane segments, and weights (line thicknesses and surface tones) and descriptions (labels) as attributes. Note that the weights themselves are also governed by descriptions.

Fig. 3.

Rule adding a cube in plan, front, and side elevation view (after Stiny, Reference Stiny1992: 425–426)

Although the dimension of the spaces is two for all three drawings, plan, front elevation, and side elevation, the three drawings define three separate spaces. The respective rules compute in parallel, however, under different transformations. Only if the algebras are defined in three dimensions, that is, U ₁₃, W ₁₃, and W ₂₃, can the algebras be selected to operate in the same 3D space and a single transformation be determined that applies to all rules in parallel. Such a solution may not always be applicable, as when working with diagrams and sketches, next to other drawings, such as plans, elevations, and even 3D models (e.g., Li, Reference Li1999).

Knight (Reference Knight2003b; see also, Krstic, Reference Krstic and Gero2012) therefore distinguishes an operation of sum of algebras from the operation of direct product on algebras. Whereas the latter allows algebras to operate in different spaces, the former requires algebras to be specified in the same space. Space is however only part of the picture; the allowable transformations matter separately as well.

Allowable transformations

Ultimately, and especially from an implementation point of view, whether algebras operate in the same space or in different spaces can be merely a visualization issue, the most important issue being whether they adopt the same or different transformations. Knight (Reference Knight2003a) even gives the following example of the component rules of a compound rule as being defined to operate in the same space: “the rules of color grammars as defined by Knight (Reference Knight1989) are compound rules with three components defined in the same 2D or 3D space: a shape component, a label component, and a color field component”. This is not entirely true; while shapes, labels, and color fields may be visually represented in the same space, they inhabit completely different spaces. A shape component exists in a 2D or 3D space, a label component inhabits a quasi-alphanumeric space, and a color field component a color space. If they would be defined – other than visualized – in the same space, then the same types of (e.g., geometric) transformations would apply to them.

Firstly, it is important to note that space is not just defined by its dimension. Jowers and Earl (Reference Jowers and Earl2015) provide an example of a 2D conic space (Fig. 4), rather than the traditional 2D Euclidean space. In a 2D conic space, line segments may become circular, elliptic, or parabolic arc segments under rotation, whereas in Euclidean space, line segments, circular, elliptic, and parabolic arc segments are all distinct under similarity transformations. This example also emphasizes the importance of the allowable transformations. If we allow affine transformations in Euclidean space, then circular arc segments can be transformed into elliptic arc segments and vice versa. Under projective transformations, circular arc segments can even be transformed into parabolic arc segments and vice versa. Nonetheless, from an implementation point of view, we can consider the shape algebras of circular, elliptic, and parabolic arc segments as distinct – or consider circular arcs as a subset of elliptic arcs, with both distinct from parabolic arcs – and consider an algebra of conic line segments as an algebraic composition under sum of all these algebras together with the shape algebra of line segments. Transformations – and spaces – that may result in interalgebra exchanges of spatial elements may then be considered separately as part of the matching mechanism.

Fig. 4.

Rotation of a line segment in a 2D conic space (after Jowers & Earl, Reference Jowers and Earl2015: 959).

Secondly, Krstic (Reference Krstic1999) points out that even if algebras operate in different spaces and allow for different sets of transformations, these sets of allowable transformations may not necessarily be independent, but may actually be related via some mapping. For example, in the case of Stiny's (Reference Stiny1992) series of cubes described in plan, front elevation, and side elevation, it may be possible to conceive of these three descriptions as projections of a 3D series of cubes, with transformations in each drawing or algebra, mapped to a single, if hypothetical, 3D transformation. However, from an implementation point of view, it may be easier to consider this mapping only implicit rather than explicit; that is, the allowable transformations for each algebra may be considered independent if only the author of the shape rules carefully considers the relationships between the three drawings, as Stiny (Reference Stiny1992) does. Krstic (Reference Krstic1999) instead offers examples where labels or descriptions change with geometric transformations. For example, building elevation labels, for example, indicating north, east, south, or west elevations may change when the building plan is rotated. Though perfectly acceptable, the problem from an implementation point of view is that such mappings are specific and cannot easily be generalized. As such, we largely ignore transformations from here on and, instead, refer to Krstic (Reference Krstic and Gero2012) for a treatment of transformations in the context of shape algebras.

Deriving algebras from two-sorted partial algebras

To derive a shape algebra from this two-sorted partial algebra, we need to distinguish the desired behavior. Each behavior results in a different derivation. Fortunately, we can reuse behaviors for different kinds of spatial or other elements. The simplest behavior is a discrete behavior, applying to both points and labels.

An algebra with carrier ℘(A) and signature including sum (“+”), product (“·”), difference (“−”), and reduce (“r”) can be defined for a discrete behavior as follows:

(1)$$\eqalign{& \forall \,X,\,Y \in \wp \,(A) \Rightarrow \cr & \quad X + Y = X \cup Y \cr & \quad X \cdot Y = X \cap Y \cr & \quad X - Y = X\backslash Y \cr & \quad r\,{\rm (}X{\rm )} = X.} $$

In a discrete behavior, the operations of sum, product, and difference correspond to the normal set operations of union, intersection, and difference. The reduce operation reduces any set to a set of maximal elements (Krishnamurti, Reference Krishnamurti1992). Under a discrete behavior, any set is maximal because any duplicates are automatically removed. The algebras U ₀ (shapes of points; we omit the dimension of the space for simplicity) and L, sets of labels, can be defined in this way (Table 2). Note that descriptions, from an algebraic point of view, behave exactly as labels, and thus, the algebra D of sets of descriptions can be defined in this way as well.

Table 2.

Algebraic operations of sum, product, and the difference for points (U ₀), labels (L), and numeric weights (N)

Before we address other spatial elements, let us first consider a behavior for weights (e.g., line thicknesses or surface tones), as is apparent from drawings on paper – a single line drawn multiple times, each time with a different thickness, appears as if it were drawn once with the largest thickness, even though it assumes the same line with other thicknesses (Stiny, Reference Stiny1992).

An algebra with carrier ℘₁(A), the set of all singleton subsets of A, and signature including sum (“+”), product (“·”), difference (“−”), and reduce (“r”) can be defined for an ordinal behavior, applying to weights, in terms of the two-sorted partial algebra with carrier {A, ℘(A)} and signature including the operations of combine, common, and complement, as follows:

(2)$$\eqalign{& \forall \,(x),\,(y) \in \wp _1\,(A) \Rightarrow \cr & \quad {\rm (}x{\rm )} + {\rm (}y{\rm )} = \hbox{combine}\,{\rm (}x,\,y{\rm )} \cr & \quad (x) \cdot (y) = \hbox{common}\,{\rm (}x,\,y{\rm )} \cr & \quad (x) - (y) = \hbox{complement}\,{\rm (}x,\,y{\rm )} \cr & \quad r\,(\{ x\} {\rm )} = {\rm \{} x{\rm \}} .} $$

For weights, we know that the result of the operations of combine, common, and complement on two singleton weights is always a singleton weight. We use this knowledge to define the operations of sum, product, and difference in terms of the operations of combine, common, and complement. Again, the reduce operation results in the argument (singleton) set itself. The algebra N of singletons of numeric weights can be defined in this way. Table 2 illustrates the algebra N if combine (x, y) ≡ max (x, y), common (x, y) ≡ min (x, y), and complement (x, y) ≡ x − y (i.e., arithmetic difference) as suggested by Stiny (Reference Stiny1992).

Deriving a shape algebra for spatial elements other than points from a two-sorted partial algebra is a little bit more complicated because of the need to consider co-equal shapes. We take a two-step approach. First, we derive a sub-algebra for co-equal shapes of spatial elements, next we define a shape algebra for a single type of spatial elements from this sub-algebra (Table 3).

Table 3.

Dependencies underlying the construction of shape algebras from sub-algebras for co-equal shapes and two-sorted partial algebras of spatial elements

A sub-algebra with carrier ℘(A) and signature including sum, product, difference, and reduce can be defined for an areal behavior as follows:

(3)$$\eqalign{& \forall \,X,\,Y \in \wp \,(A):\,\,\forall \,x \in \,X,\,\forall \,y \in Y,\,\hbox{co}(x) = \hbox{co}(y) \Rightarrow \cr & \quad \hbox{sum}\,{\rm (}X,\,Y{\rm )} = \hbox{construct}\,(\hbox{outside}\, (b\, (X{\rm )},\,Y )\,\cup \cr &\qquad \hbox{outside}\, (b\, (Y ),\,X)\, \cup \,\hbox{same - side}\, (b\, (X),\,b\, (Y))) \cr & \quad \hbox{product}\, (X,\,Y) = \hbox{construct}\,(\hbox{inside}\, (b\, (X),\,Y)\, \cup \cr &\qquad \hbox{inside}\, (b\, (Y),\,X)\, \cup \,\hbox{same - side}\, (b\, (X),\,b (Y))) \cr & \quad \hbox{difference}\, (X,\,Y) = \hbox{construct}\,(\hbox{outside}\, (b\, (X),\,Y)\, \cup \,\cr & \qquad \hbox{inside} (b\, (Y),\,X)\, \cup \,\hbox{opposite - side}\, (b\, (X),\,b\, (Y))) \cr & \quad \hbox{reduce}\, (X) = \hbox{sum}\, ((x),\,\hbox{reduce}\, (X\backslash x))\,\qquad\hbox{if}\,\,\exists \,x \in X \cr &\qquad \emptyset \qquad \qquad \qquad \qquad \hbox{otherwise.}} $$

Instead of comparing the shape operands for coequality, it is checked that all spatial elements are co-equal, that is, share the same (co-)descriptor (“co”). Then, the operations can be expressed in terms of the boundaries (“b”) of each (co-equal) shape (Krishnamurti & Stouffs, Reference Krishnamurti and Stouffs2004; Stouffs & Krishnamurti, Reference Stouffs and Krishnamurti2006). Specifically, “outside(b(X), Y)” returns the collection of boundaries of shape X that lie outside of shape Y. Similarly, “inside(b(Y), X)” returns the collection of boundaries of shape Y that lie inside of shape X. “same-side(b(X), b(Y))” denotes the collection of boundaries that belong to both shapes X and Y, and where the interiors of the respective shapes lie on the same side of the boundary, and “opposite-side(b(X), b(Y))” denotes the collection of boundaries that belong to both shapes X and Y, and where the interiors of the respective shapes lie on opposite sides of the boundary. Then, the boundary of the shape resulting from the co-sum operation is formed by the “outside(b(X), Y)”, “outside(b(Y), X)”, and “same-side(b(X), b(Y))” collections, and the shape can be constructed from the union of these collections. The product and difference operations are similarly defined (Table 4). In the case of the reduce operation, each spatial element in the shape is summed with the reduced remainder of the shape. From an implementation point of view, this recursive definition of reduce is certainly not the most efficient; actually, the same can be said about the other operations – the classification of boundary segments with respect to another shape can be computed once for all of the classes inside, outside, same-side, and opposite-side. Obviously, these definitions only serve as abstractions of the actual procedures, we refer to Stouffs and Krishnamurti (Reference Stouffs and Krishnamurti2006) for actual, and efficient, algorithms.

Table 4.

Classified boundary segments that make up the shape resulting from a shape operation

Then, a shape algebra with carrier ℘(A) and signature including sum (“+”), product (“·”), difference (“−”), and reduce (“r”) can be defined for an areal behavior, applying to line segments, plane segments, and volumes, in terms of the sub-algebra with carrier ℘(A) and signature sum, product, difference, and reduce, as follows:

(4)$$\eqalign{& \forall \,X,\,Y \in \wp \,(A) \Rightarrow \cr & \quad X + Y = r\,(X \cup Y) \cr &\quad X \cdot Y = \cup _{\rm c}\hbox{product}\,(\{ x \in X:\,\hbox{co(}x) = c\}, \cr &\qquad \{ y \in Y:\,\hbox{co(}y) = c\}) \cr & \quad X - Y = \cup _{\rm c}\{ x \in X:\,\hbox{co(}x) = c \wedge \neg \exists \,y \in Y:\,\hbox{co(}y) = c\} \cup \cr & \cup_{\rm c} \hbox{difference}\,(\{ x \in X:\,\hbox{co(}x) = c\}, \{ y \in Y:\,\hbox{co(}y) = c\}) \cr & \quad r (X) = \cup _{\rm c}\hbox{reduce} \; (\{ x \in X:\,\hbox{co(}x) = c\}).} $$

The operations of product, difference, and reduce are expressed directly in terms of the similar operations on the respective sub-shapes of co-equal spatial elements. In the case of complement, shapes of co-equal spatial elements from X for which there exists no (co-equal) shape in Y also form part of the result. A similar approach can be taken for the operation of sum; however, for simplicity, we prefer to express the operation of sum in terms of the operation of reducing (“r”) on the combined sets of spatial elements. Note that the algebras U ₁ (shapes of line segments), U ₂ (shapes of plane segments), and U ₃ (shapes of volumes) can all be defined in this way (Table 5).

Table 5.

Algebraic operations of sum, product, and difference for plane segments (U ₂) and volumes (U ₃)

We should note that while an areal behavior applies to shapes of line segments as well, from an implementation point of view, it is more efficient to define an interval behavior for shapes of line segments. Additionally, while these behaviors cover shapes of different kinds of spatial elements and sets of labels and singletons of weights, other behaviors can be identified to apply to other kinds of attributes, for example, for material rankings (Knight, Reference Knight1993). Instead, we continue with the derivation of compositions of algebras under the direct product.

Deriving algebras with sum and direct product

The operations of sum and direct product apply to all algebras that share the same signature, specifically, the algebras U ₀, U ₁, U ₂, U ₃, L, D, and N we have previously defined. We address some implications of this in the section Algebraic Compositions. Here, we define an (shape) algebra with carrier ℘(A) × ℘(B) and signature including sum (“+”), product (“·”), difference (“−”), and reduce (“r”) in terms of the (shape) algebras with carriers ℘(A) and ℘(B) and identical signatures, as follows:

(5)$$\eqalign{& \forall (A_{\rm 1},\,B_{\rm 1}),\,(A_{\rm 2},\,B_{\rm 2}) \in \wp (A) \times \wp (B) \Rightarrow \cr & \quad (A_{\rm 1},B_{\rm 1}) + (A_{\rm 2},B_{\rm 2}) = (A_{\rm 1} + A_{\rm 2},\,B_{\rm 1} + B_{\rm 2}) \cr & \quad (A_{\rm 1},\,B_{\rm 1}) \cdot (A_{\rm 2},\,B_{\rm 2}) = (A_{\rm 1} \cdot A_{\rm 2},\,B_{\rm 1} \cdot B_{\rm 2}) \cr & \quad (A_{\rm 1},\,B_{\rm 1}) - (A_{\rm 2},\,B_{\rm 2}) = (A_{\rm 1} - A_{\rm 2},\,B_{\rm 1} - B_{\rm 2}) \cr & \quad r (A_{\rm 1},B_{\rm 1}) = (r (A_{\rm 1}),\,r (B_{\rm 1})).} $$

An algebra of shapes of points and line segments, U ₀ + U ₁, and an algebra of shapes of line segments and sets of descriptions, U ₁ × D, among others, can be defined in this way. We do not distinguish algebraic derivations under sum or direct product, the distinction only relates to the (embedding) space(s) and the allowable transformations, neither of which is considered here. As we have mentioned before, for algebraic compositions under the direct product – and sum – shape rules and the operations of sum, product, and difference (and reduce) operate in parallel on the various component algebras.

Deriving augmented shape algebras

Next, we can tackle the issue of an algebra for shapes augmented with attributes. When defining the augmented shape algebra, rather than referring to a shape algebra and an attribute algebra (e.g., L or N), we instead refer to the partial algebra for the spatial elements, just as we define the shape algebra from the partial algebra of its spatial elements. That is, we derive the operational behavior of an augmented shape algebra from the behaviors of the partial algebra of spatial elements and the algebra of attribute elements. For example, consider two co-equal line segments that overlap. Without attributes, these combine into a single line segment. With attributes, they combine only if they share the same attributes or, otherwise, if the segments are identical. In the latter case, the attributes combine. Otherwise, different segments (or parts thereof) necessarily have different attributes and need to be represented separately (Table 6). The behavior of the attribute elements, for each of the different line segments (or parts thereof), however remains the same.

Table 6.

Two spatial elements (line segments) combine depending on whether their attributes combine

An algebra with carrier ℘[A × ℘(B)] and signature including sum (“+”), product (“·”), difference (“−”), and reduce (“r”) can be defined for a discrete behavior, in terms of the (attribute) algebra ℘(B) with signature including sum (“+”), product (“·”), difference (“−”), and reduce (“r”), as follows:

(6)$$\eqalign{& \forall \,X,\,Y \in \wp \,[A \times \wp (B)] \Rightarrow \cr & \quad X + Y = \{ (x,\,B_x + B_y):\,(x,\,B_x) \in X \wedge (x,\,B_y) \in Y\}\cup \cr & \quad \quad \quad \{ (x,\,B_x):\,(x,B_x) \in X \wedge \neg \exists (x,B_y) \in Y\} \cup \cr & \quad \quad \quad \{ (y,\,B_y):\,(y,\,B_y) \in Y \wedge \neg \exists (y,\,B_x) \in X\} \cr & \quad X \cdot Y = \{ (x,\,B_x \cdot B_y):\,(x,\,B_x) \in X \wedge (x,\,B_y) \in Y\} \cr & \quad X - Y = \{ (x,\,B_x - B_y):\,(x,\,B_x) \in X \wedge (x,\,B_y) \in Y\} \cup \cr & \quad \quad \quad \{ (x,\,B_x):\,(x,\,B_x) \in X \wedge \neg \exists (x,\,B_y) \in Y\} \cr & \quad r (X) = \{ (x,\,B_x)\} + r[\hbox{X}\backslash (x,\,B_x)]\,\qquad \hbox{if}\,\,\exists (x,\,B_x) \in X \cr & \quad\qquad \emptyset \qquad \qquad \qquad \hbox{otherwise.}} $$

Comparing this to the discrete behavior for a non-augmented shape algebra, we observe that for the operations of sum, product, and difference, if a spatial element is shared between both (augmented) shapes, we combine both attributes of the spatial element under the same operation. In the case of the operations of sum and difference, we may need to add spatial elements, with their original attribute, that belong to one augmented shape but not the other. We express the operation of reducing in terms of the operation of sum for the same algebra. An algebra of shapes of labeled points, V ₀, or shapes of weighted points, W ₀, with the weights representing gray scales or diameters, can be defined in this way (Table 7).

Table 7.

Algebraic operations of sum, product, and difference for labeled points (V ₀) and weighted line segments (W ₁)

Having established an augmented shape algebra for points, a demonstration of an augmented shape algebra for other spatial elements, using the areal behavior, remains. In fact, where the shape algebra for an areal behavior is expressed in terms of the sub-algebra for co-equal shapes, we can define a sub-algebra for co-equal augmented shapes and retain the definition for the shape algebra as a definition for the augmented shape algebra as well. The only difficulty is the use of the (co-)descriptor function (“co”) on elements of the shape algebra. However, if we overload the function “co” to accept augmented spatial elements, that is, elements with attributes, then there is no issue.

A sub-algebra with carrier ℘(A × ℘(B)) and signature including sum, product, difference, and reduce can be defined for an areal behavior, in terms of the two-sorted partial algebra with carrier {A, ℘(A)} and signature including the operations of combine, common, and complement, and the (attribute) algebra ℘(B) with signature including sum (“+”), product (“·”), difference (“−”), and reduce (“r”), as follows:

(7)$$\eqalign{& \forall X,Y \in \wp (A \times \wp (B)):\forall (x,\,B_x) \in X,\,\forall (y,\,B_y) \in Y,\cr &\quad \hbox{co(}x) = \hbox{co(}y) \Rightarrow\cr & \quad \hbox{sum}\, (X,\,Y) = \hbox{consolidate}\,(\hbox{common-with-sum}\, (X,Y) \cup \cr & \quad \hbox{remainder}\, (X,\,Y) \cup \hbox{remainder}\, (Y,X)) \cr & \quad \hbox{product}\, (X,\,Y) = \hbox{consolidate(} \cr &\quad {\rm common} \hbox{-} {\rm with}\hbox{-}{\rm product}\, (X,\,Y)) \cr & \quad \hbox{difference}\, (X,\,Y) = \hbox{consolidate(}\, \cr & \quad {\rm common \hbox{-} with \hbox{-} difference}\, (X,\,Y) \cup\cr &\quad \hbox{remainder}\, (X,\,Y)) \cr & \quad \hbox{reduce}\, (X) = \cr &\quad \hbox{sum}\,(\{ (x,\,B_x)\}, \hbox{reduce}\,(X\backslash (x,\,B_x)))\,\quad \hbox{if}\,\,\exists \,(x,\,B_x) \in X \cr & \quad \qquad \emptyset \qquad \qquad \qquad \hbox{otherwise.}}$$

We cannot simply determine a resulting shape – from one of the operations of sum, product, and difference – from the classification of the boundaries of both (co-equal) shapes in their entirety. Instead, we need to apply the classification and construction to each pair of spatial elements from the respective shapes, in order to be able to assign the appropriate attributes – as defined by the operation of sum, product, or difference applied to the respective attributes of the spatial elements. Basically, we split any overlapping spatial elements into their common parts and the remaining complement parts. For each common part, we combine the attributes under the respective operation of sum, product, or difference. This is expressed by the helper functions “common-with-sum”, “common-with-product”, and “common-with-difference”; these return the common shape of two co-equal spatial elements, with as attribute, respectively, the sum, product, and difference of the respective attributes (see below for their specification). Any complement part that forms part of the result naturally retains its attribute; the helper function “remainder” returns the complement shape of a spatial element with respect to a co-equal shape, with as attribute the original attribute of the spatial element. Finally, we combine any parts that end up having the same attribute. The helper function “consolidate” is a variant of the operation of reducing (for a co-equal shape) that assumes that none of the spatial elements overlap, though they may share boundaries. It recursively tries to find two spatial elements that share a boundary and also share the same attribute(s), in which case it combines both elements into a single element.

$$\eqalign{& {\rm common \hbox{-} with \hbox{-} sum}\, (X,Y) = \cup _x \cup _y\{ (z,\,B_x + B_y): \cr & \quad (x,\,B_x) \in X \wedge (y,\,B_y) \in Y \wedge z \in \hbox{common}\, (x,\,y)\} \cr &{\rm common \hbox{-} with \hbox{-} product}\, (X,\,Y) = \cup _x \cup _y\{ (z,\,B_x \cdot B_y): \cr & \quad (x,\,B_x) \in X \wedge (y,\,B_y) \in Y \wedge z \in \hbox{common}\, (x,\,y)\} \cr & {\rm common \hbox{-} with \hbox{-} difference}\, (X,\,Y) = \cup _x \cup _y\{ (z,B_x - B_y): \cr & \quad {\rm} (x,B_x) \in X \wedge (y,B_{\scriptstyle y \hfill \atop \scriptstyle \hfill}) \in Y \wedge z \in \hbox{common}\, (x,\,y)\} \cr & \hbox{remainder}\, (X,\,Y) = \cup _x\{ (z,\,B_x):(x,\,B_x) \in X \wedge \cr & \quad z \in \hbox{difference}\, (\{ x\}, \,Y)\} \cr & \hbox{consolidate}\, (X) = \cr & \quad \hbox{consolidate}\,(\{ (z,\,B_x)\} \cup X\backslash {\rm} \{ (x,\,B_x),(y,\,B_x)\}) \cr & \quad\quad \hbox{if}\,\exists \,(x,\,B_x) \in X \wedge (y,\,B_x) \in X: x \ne y \wedge \cr & \quad \ \{ z\} = \hbox{combine}\, (x,\,y) \cr & \quad X \qquad \hbox{otherwise}{\rm.}} $$

An algebra of shapes of weighted line segments, W ₁, or shapes of labeled plane segments, V ₂, among others, can be defined in this way (Table 7).

Attribution

The derivation of augmented (shape) algebras distinguishes between shape algebras and attribute algebras. However, considering that shape and attribute algebras may share the same behavior, and no conditions have been specified on the behavior of the attribute algebra component of the augmented (shape) algebra, the derivations above equally apply to augmented algebras where the base component is a two-sorted partial algebra for, for example, labels, and the attribute algebra is a shape algebra. Thus, we can conceivably write L ∧ U ₀, an algebra for sets of labels with points as attributes. In fact, any two-sorted partial algebra can be considered as a base component and any basic algebra as attribute algebra, allowing for combinations of shapes and non-geometric attributes, non-geometric elements and shape attributes, shapes and shape attributes, and non-geometric elements and non-geometric attributes (see Fig. 5, right). For example, Stouffs et al. (Reference Stouffs, Krishnamurti and Park2007) consider algebras of shapes as attribute algebras, with the base algebra defined as an algebra of labels, for example, L ∧ U ₃. As an example, the primary information target of a planner of embedded sensors is the building slab for the sensor to be embedded, for example, represented as a label identifying the slab, while the shape of the slab is also required, but only as an attribute to the label. While visual calculating (Stiny, Reference Stiny2006) is a powerful paradigm for design, design actions also move beyond the visual, requiring alternative design representations that primarily target non-spatial data elements. A truly algebraic approach to representing design data enables such flexibility. For example, consider the algebra D of sets of descriptions. While most authors consider descriptions parallel to drawings, for example, U ₁ × D, Beirão (Reference Beirão2012) considers descriptions as attributes to spatial objects, allowing for objects to refer to alternate, though similar, descriptions, thus, U ₁ ∧ D.

Returning to the derivation of augmented algebras, admittedly, we did not specify an augmented algebra for an ordinal behavior. This, however, can be easily done and we leave it as an exercise to the reader. Similarly, other behaviors can also be considered, such as an enumerative behavior allowing for Knight's (Reference Knight1989, Reference Knight1993) color grammars. Knight (Reference Knight1993) considers explicit rankings for colors, materials, or other qualitative aspects of design. Alternatively, we can consider a behavior for colors based on a method to combine RGB or HSV color values and determine the common and complement value of one color with respect to another. Whichever approach we adopt to represent colors, once we have defined a basic algebra of sets of colors, C, we can define an algebra of shapes of colored plane segments as U ₂ ∧ C.

One step further, the derivation of augmented algebras does not restrict the attribute component to only basic algebras. Any algebra ℘(B) with signature including sum, product, difference, and reduce can serve as attribute component, including both augmented and compound algebras. As such, we can define an attribute algebra as the sum of two basic attribute algebras, for instance, U ₀ ∧ (L + N) defines an algebra of shapes of points with both labels and weights (e.g., diameters or gray scales) as attributes. Intuitively, we can consider the operation of sum to distribute over the operation of attribution, that is, U ₀ ∧ (L + N) = (U ₀ ∧ L) + (U ₀ ∧ N) = V ₀ + W ₀, combining points with labels and points with weights.

A hierarchical modus operandi

Considering these extensions, it is useful to further explore how the different operations on algebras – direct product, sum, and attribution – may combine to define compound algebras, and thus, shape grammars. From an algebraic point of view, the base component of an augmented algebra should always be a two-sorted partial algebra, and therefore, we restrict the base operand under attribution to a basic algebra. That is, we never write (U ₀ + U ₁) ∧ L but, instead, always (U ₀ ∧ L) + (U ₁ ∧ L). Secondly, though operations of sum and the direct product could be mixed indiscriminately from an algebraic point of view, conceptually, we may choose to restrict the use of the direct product to the first – outer – compositional layer (Fig. 6). That is, we take the point of view that each (compound) algebra under the operation of direct product represents a different drawing or information view and that all the information within the same drawing or view (corresponding to different component algebras within a composition under sum) adopts a single set of allowable transformations for rule application. The opposite, an algebra that is composed under direct product and itself takes part in a composition under sum is not feasible; it is impossible to determine which transformation(s) applies over the composition under sum, since at least one component – the composition under direct product – is unequivocal on the transformation(s) to apply. Obviously, when we write L + N or U ₁ + D, clearly, we are not assuming that labels and weights populate the same space or, similarly, for line segments and descriptions, nor that their respective sets of allowable transformations necessarily must be related via some mapping. Within a single set of allowable transformations for rule application, subsets of allowable transformations can be identified for different algebras. In fact, even if neither labels nor weights allow any transformations, we can consider them under the operation of sum. The operation of sum simply implies that if two algebras allow for the same set of transformations, for example, similarity transformations for geometric elements, including points, lines segments, and plane segments, they must share the same transformation – under rule application. Otherwise, they can simply operate in parallel under different transformations, or none at all.

Fig. 6.

A hierarchical modus operandi for the three operations on algebras, direct product (“×”), sum (“+”), and attribution (“∧”). Basic algebras are indicated with a black core; ellipses mark undeveloped branches that are, each, similar to the developed branch for the same operation (as illustrated with a miniature expansion); each developed branch indicates its potential cardinality.

The same applies for attribute shape algebras. Referring once again to the example of the embedded sensor view presented by Stouffs et al. (Reference Stouffs, Krishnamurti and Park2007), a more extensive information list required by the embedded sensor planner includes the target slab, represented as a label or description, its shape, represented as a volume in U ₃₃, its material type, again represented as a label or description, and its location, represented as a point in U ₀₃. The corresponding algebra for this view may be defined as L ∧ U ₃₃ ∧ L ∧ U ₀₃. Although the algebras U ₃₃ and U ₀₃ are related through attribution, rather than the sum, they should still be considered as using the same space, not just from a dimensional point of view. In this example, it makes perfect sense that the shape and location of the slabs are subject to the same transformation.

Thus, if the first – outer – compositional layer is specified under the operation of the direct product, its components can be algebraic compositions under sum, augmented algebras and/or basic algebras, together defining a second compositional layer (Fig. 6). Compositions under sum are themselves made up of augmented algebras and/or basic algebras; while, augmented algebras are considered – informally – as compositions of a basic algebra as a base and either a composition under sum, an augmented algebra, or a basic algebra as an attribute (Fig. 6). Some implications of this hierarchical modus operandi are discussed below.

Though the definition of a compound algebra under sum or direct product (5) is strictly speaking non-commutative, intuitively, and from an implementation point of view, it makes more sense to consider the operations of sum and direct product on algebras as both commutative and associative, for example, for an algebra of shapes of labeled points and line segments, V ₀ + U ₁ = U ₁ + V ₀, and for an algebra of shapes of labeled points, weighted line segments, and plane segments, V ₀ + (W ₁ + U ₂) = (V ₀ + W ₁) + U ₂ = V ₀ + W ₁ + U ₂. Hereto, we can consider a canonical ordering of the component algebras within a composition under sum or direct product. On the other hand, the operation of attribution on algebras is, obviously, non-commutative and, considering that the base is in fact defined as a two-sorted partial algebra, non-associative. Given a basic algebra of sets of colors, C, we might conceive of an algebra U ₀ ∧ L ∧ C. This should always be interpreted as U ₀ ∧ (L ∧ C), that is, an algebra of points, where each point has one or more labels and each label has a color specified. If we want colors to be applied to points instead, we can write U ₀ ∧ (L + C), and if we want colors to be applied to both points and labels, we must write U ₀ ∧ [(L ∧ C) + C]. As a consequence of the latter, the points might exhibit different colors than the points’ labels. Any constraints relating the labels’ colors to the points’ colors would have to be specified outside of the algebraic composition, for example, in the shape rules and initial shape. We already considered the operation of sum to distribute over the operation of attribution, that is, U ₀ ∧ (L + N) = (U ₀ ∧ L) + (U ₀ ∧ N) = V ₀ + W ₀, combining points with labels and points with weights. Considering the hierarchical modus operandi, other distributive rules do not apply.

Finally, let us revisit Stiny's (Reference Stiny1992) example describing a series of cubes separately in the plan, in front elevation, and in side elevation. While the resulting shapes are presented in the algebras U ₁₂, W ₁₂, and W ₂₂, respectively, the shape rules additionally make use of labeled points – and line segments in the case of the side elevation – to constrain rule application. That is, the shape rules are presented in the algebras U ₁₂ + V ₀₂, W ₁₂ + V ₀₂, and W ₂₂ + U ₁₂ + V ₀₂, respectively. Considering that Stiny (Reference Stiny1992) only considers the operation of direct product on algebras, he poses the question whether to write (U ₁₂ × V ₀₂) × (W ₁₂ × V ₀₂) × (W ₂₂ × U ₁₂ × V ₀₂) – with or without parentheses – or to simplify this as U ₁₂ × V ₀₂ × W ₁₂ × W ₂₂. However, when we distinguish the operations of sum and direct product on algebras and adopt the operation of direct product exclusively to combine different drawings or information views, the question is no longer pertinent and the only appropriate representation is the compound algebra (U ₁₂ + V ₀₂) × (W ₁₂ + V ₀₂) × (W ₂₂ + U ₁₂ + V ₀₂). Here the parentheses are only added for readability.

The matching problem

One consequence of supporting a wide variety of algebraic compositions within a single shape grammar interpreter is that the process of determining the applicable transformation(s) for shape rule application is further complicated from what was already far from straightforward. Recall that a shape rule a→b applies to a shape s if there exists a (similarity) transformation t such that t(a) ≤ s. Let us assume we have a shape rule defined over an algebraic composition under sum and attribution. Obviously, if the algebraic composition is defined under the direct product, we can split the problem into a few smaller problems, one for each algebraic component under the direct product, as the operation of direct product assumes each component to apply under a different transformation.

Krishnamurti and Stouffs (Reference Krishnamurti and Stouffs1997) consider all possible cases of applicable similarity transformations for shapes in the algebra U ₀ + U ₁ + U ₂ + U ₃, or any other combination of U ₀, U ₁, U ₂, and/or U ₃ under sum. These include both determinate, resulting in a finite number of possible transformations, and indeterminate, allowing for an infinite number of possible transformations, cases, based on the process of identifying and constructing distinguishable elements – that is, spatial elements (points, lines, or planes) the properties of which (with respect to the original shape) are preserved under the part relation and (similarity) transformations. They also allude to the fact that these spatial elements may have attributes assigned.

However, from an implementation point of view, whether spatial elements have attributes and the kind of attributes can make an important difference in the efficiency of the matching algorithm. After all, it is not just important to find all possible transformations under which the left-hand side of a shape rule is a part of a given shape s, this should also be done efficiently within a reasonable time frame. For instance, labeled points are often considered in shape grammars in order to reduce the number of possible ways – that is, transformations – under which a rule may apply to a given shape. Labeled points are particularly appropriate for this as both labels and points adhere to a discrete behavior. As labels only match if they are identical, labeled points only match if they have identical labels. Furthermore, three non-collinear distinguishable points – additional distinguishable points can be constructed as the intersection points of two lines, or a line and a plane, or two lines of intersection of corresponding planes, or as the feet of the common perpendicular of two skew lines – is the only possible determinate case in three dimensions. As such, considering labeled points not only reduces the number of possible transformations, labeled points can also be considered preferential in the process of determining distinguishable elements in order to address the matching problem.

In general, we may conclude that both a basic algebra's behavior and the allowable transformations for the algebra contribute to the preferential applicability of this particular basic algebra in the process of determining applicable transformations for shape rule application. Thus, we can prioritize the basic algebras that participate in an algebraic composition under sum and attribution for their role in the process of determining applicable transformations based on these two characteristics. We suggest a classification of basic algebras based on their behavioral specification into four classes: discrete, linear, planar, and spatial. These may be assigned consecutive numeric values, such as 0, 1, 2, and 3, respectively. Obviously, both points (U ₀) and labels (L), as well as (textual) descriptions (D) adhere to the discrete class. Other spatial and non-spatial elements that can be classified as discrete are circles, ellipses, infinite lines, numeric labels, images, dates, enumerative values, etc. Line segments (U ₁) and weights (N) form part of the linear class, as do any other elements that adhere to a linear dimension, such as, circular arcs and elliptical arcs, but also time periods and, potentially, colors (C). Whether colors are classified as linear or, instead, spatial, is dependent on their exact behavioral specification. Classifying colors as linear reflects on Knight's (Reference Knight1993) adoption of notions of transparency, opacity, and ranking to regulate the behavior of colors on interacting areas or volumes. Finally, plane segments (U ₂) and volumes (U ₃) are classified as planar and spatial, respectively.

Additionally, we suggest a classification of allowable transformations by their degrees-of-freedom. Similarity transformations demonstrate seven degrees-of-freedom: three translational, three rotational, and one (uniform) scaling. These can apply to all spatial elements. Most other basic algebras do not allow for any transformations, the corresponding degrees-of-freedom is therefore zero. However, if case transformations would be considered for labels, the degrees-of-freedom could be specified as one. This is also the case for descriptions, even if case transformations for descriptions are not considered. Description rules (or a description function, Stiny, Reference Stiny1981) allow for parameters to be specified in the left-hand side of the rule, thereby allowing for more than an exact match. Such necessitates a degrees-of-freedom of at least one. Considering these classifications of behavioral specifications and allowable transformations, a simple prioritization of basic algebras in the process of determining applicable transformations can be based on adding both values, the numeric value of the behavioral class and the degrees-of-freedom, together. In addition, if relevant, the algebraic operation under which the basic algebra forms part of the overall algebraic composition, that is, sum or attribution, can be considered as an additional prioritization factor.

For example, an algebra of labeled points and line segments distinguishes three basic algebras: labels, points, and line segments. Points and labels are classified as discrete, with value 0, while line segments belong to the linear class, with value 1. Labels may not allow for any transformations, specifying the degrees-of-freedom as 0; whereas points and line segments, under similarity transformations, have 7 degrees of freedom. Adding the classification value and the degrees-of-freedom results in prioritization values of 0, 7, and 8 for labels, points, and line segments, respectively. Thus, when determining potential matches, we can first look for different labels on the left-hand side of the shape rule. Obviously, if all points share the same label, there is little benefit in considering the labels and the problem remains to identify three distinguishable points. Assuming there are (at least) three distinguishable (labeled) points in the left-hand side of the shape rule and n distinguishable (labeled) points in the shape under rule application, the number of combinations of points matching the three selected points from the left-hand side equals $\left( \matrix{ n \cr 3} \right) = \displaystyle{n! \over 3!(n - 3)!} = (1/6)n\,(n - 1)\,(n - 2) = O\,(n^3)$. This assumes all points share the same label or no label at all. However, if there are three (or more) distinct labels in the left-hand side of the shape rule, we can classify the points by their label and select just one point per label. Assuming an equal distribution of the labeled points over the three distinct labels in the shape under rule application, the number of combinations of points matching the three selected points equals $3\left(\matrix{n/3 \cr 1} \right) = \displaystyle{3\,(n/3)! \over 1!((n/3) - 1)!} = n$. We only need to consider each point once. Note that this holds even if the labeled points are not evenly distributed per label. Thus, the presence of three distinct labels reduces the number of combinations from O(n ³) to O(n). Similarly, if there are only two distinct labels, the number of combinations is reduced to O(n ²), which is potentially still an important improvement. Note that this applies as well for labeled line segments, with distinguishable points (e.g., intersection points) classified by the label. Obviously, considering that points have a lower prioritization value than line segments, 7 versus 8, gives preference to labeled points over labeled line segments. Similarly, if we assume an algebra of labeled points and line segments as well as labels, U ₀ ∧ L + U ₁ + L, we must prioritize between the two basic algebras of labels. Considering that one instance stands on its own under the operation of sum, while the other is part of an augmented algebra (under attribution), the latter receives higher priority among the two. That is perfectly logical; while the attribute labels constrain the similarity transformations over points and line segments, the other labels can only serve to constrain transformations over labels, if any.