This section reviews implemented viewpoints in knowledge representation systems. Knowledge representation is almost synonymous with AI and the systems discussed come from widely differing areas although they all share a common trait - viewpoints.
Viewgen is a ‘belief engine’ built to demonstrate the first steps towards an artificial believer by embodying an algorithm for belief ascription [Ballim & Wilks, 1991].
Any system that is designed to engage in dialogue with other agents must reason about them and their beliefs [Ballim, Wilks & Barnden, 1991; Wilks & Ballim, 1987; Wilks & Bien, 1983] In Viewgen these requirements
Man(John) Height_of(John)=6-feet John system Earth John Flat(Earth)
Figure 2.2 Beliefs about and of John from [Ballim, Wilks & Barnden, 1991]
are satisfied through the use of environments which partition the beliefs the system holds. Environments are groups of propositions and exist in two forms: viewpoints, representing a particular agent’s point of view, and topics, containing beliefs relevant to a given subject [Ballim & Wilks, 1991]. Viewpoints are constructed according to a rule of belief ascription:
The default ascriptional rule is to assume that another person’s view is the same as one’s own except where is explicit evidence to the contrary.
[Ballim & Wilks, 1991]
Figure 2.2 shows a pictorial representation of beliefs about and of John [Ballim, Wilks & Barnden, 1991]. Boxes with labels at the bottom are viewpoints or believer environments. All beliefs lie within the system viewpoint. Viewpoints contain topic environments, e.g. ‘John’ and ‘Earth’, shown as boxes with labels at the top left. So the system believes that John is a man and that he is six feet tall. As the topic environment 'John' is a person it can contain beliefs held by that person as well as beliefs about John. So within the topic environment is the (nested) viewpoint ‘John’ which contains the topic environment ‘Earth’ and the belief that the earth is flat. In other words, the system has beliefs about a six foot tall man called John who believes the earth is flat.
Viewpoints are generated on demand, rather than pre-stored, by ascribing beliefs from one viewpoint to another using the default rule mentioned above. Viewgen is at present not integrated with a natural language interface, although this is a long term aim.
Spaceprobe
Spaceprobe is a computational knowledge representation system that embodies the theory of Partitioned Representations [Dinsmore, 1991].
In partitioned representations the information communicated in a discourse or acquired by other means is distributed in a principled way over a large set of
spaces, each of which defines a local, or parochial, domain of reasoning.
[Dinsmore, 1991] pp 45
Partitioned Representations are intended to be both a system of mental representation and a means of understanding natural language. Processing is performed in spaces that are created in response to ‘certain morphemes and syntactic constructions’ [Dinsmore, 1991] pp119 such as: ‘Warren believes’, ‘If Bush has a dog’, ‘In the film’, ‘In Spain’, ‘In 1935’ etc. These cues are similar to the space builders of the Mental Spaces theory presented in [Fauconnier, 1985], see section 3.4.
Spaces are organised into a hierarchy and accessed via a context such as ‘Warren believes’ in a similar way to the viewpoints in Viewgen (see above). Reasoning is local to a space although the results in child spaces can be raised into parent spaces using the context: this is called context climbing. The context can be thought of as similar to an ‘application heuristic’ [Moyse, 1992] in that it determines when a space should be used. Spaceprobe also uses various activations for its spaces depending on how recently they have been used in discourse processing.
Partitioned Representations are basically a structured set of small knowledge bases with information distributed between the content and organisation of the spaces. The parochial reasoning used is the same in all spaces. Although the motivation behind Spaceprobe and Partitioned Representations seems to be linguistic they illustrate the power that can be derived from moving away from an unstructured knowledge base.
MULTILOG, OMEGA and IM2
MULTILOG is a logic programming language for knowledge representation based on worlds [Kaufmann & Grumbach, 1986]. Worlds consist of a set of clauses and an inference mechanism and can be linked together via inheritance relations.
MULTILOG extends the logic-based OMEGA language [Attardi & Simi, 1984] and partitioned semantic networks [Hendrix, 1979] by providing local inference mechanisms for each world. [Kaufmann & Grumbach, 1986] provide an example involving diagnosing a logic circuit which shows that MULTILOG is capable of hypothetical reasoning and of using different strategies (forward and backward chaining) in different worlds.
In OMEGA a viewpoint is defined as being a set of assumptions and as in MULTILOG viewpoints can be related to other viewpoints, e.g. viewpoint vp1 is a subset of viewpoint vp2. [Attardi & Simi, 1984] outline an example in which OMEGA solves the Wise Men Puzzle8 by assigning a single viewpoint to each wise man. In addition there is a viewpoint of the real world which acts as an oracle, or truth source9. [Attardi & Simi, 1984] claims that the OMEGA framework allows different situations at different times to be represented without resorting to temporal logic.
IM2 [Emde, 1987] is a knowledge representation system based on worlds which are similar to the viewpoints of OMEGA.
‘The most notable difference between worlds and [OMEGA] viewpoints is that information can be only added but not changed within viewpoints. If a description in OMEGA must be changed, then a new viewpoint has to be created.’
[Emde, 1987]
IM2 worlds are used to separate knowledge of different generality, declarative and procedural knowledge and also to allow the system to represent competing models of a domain and to run ‘contests’ between them. IM2 provides world inheritance in a similar way to OMEGA and MULTILOG but also uses evidence points to represent uncertainty and so allows a world to contain contradictory information.
SNeBR
SNeBR (SNePS10 with Belief Revision) [Martins & Shapiro, 1988] is an implementation of MBR (Multiple Belief Reasoner) [Martins & Shapiro, 1983], an abstract belief revision system.
8 [Attardi & Simi, 1984] state the puzzle as: ‘A king wishing to know which of his men is the wisest puts a white hat on each of their heads, tells them that at least one hat is white, and asks the first to tell the colour of his hat. The man answers that he does not know. The second man gives the same answer to the same question. The third man answers that his hat is white. The puzzle is: how did the third man know his hat was white?’
9 [Ballim, 1991, pp 208] criticise this element of the OMEGA system as being unrealistic and present an alternative solution to the Wise Men Puzzle.
MBR works with contexts, sets of hypotheses, which determine belief spaces, the hypotheses plus all propositions derived exclusively from them. Each operation is performed relative to a current context and a current belief space. There are no restrictions on the content or subject of hypotheses in a context other than that they are not known to be inconsistent.
SNeBR integrates both forward and backward inference in a context although all contexts use the reasoning of the logic SWM11 [Martins & Shapiro, 1988]. Belief revision is done interactively by the user by defining which hypotheses should be present in a context.
IDM
IDM (Integrated Diagnostic Model) [Fink & Lusth, 1987] is an expert system that combines two forms of knowledge, experiential and fundamental, in the domain of diagnosis and repair of electrical devices.
Experiential knowledge is the shallow empirical knowledge that an expert acquires over a period of time based on experience. ... Fundamental knowledge is the deeper model-oriented knowledge one usually acquires early on in training. It often comes from books and tends to be based on the structure and function of the device.
[Fink & Lusth, 1987]
The two knowledge bases have independent inference engines and their results are combined via an executor. The executor has a further knowledge base which provides the means of integrating the two experiential and fundamental knowledge bases. So IDM can function in situations where it is lacking device knowledge or experience.
Compositional Modeling
Compositional M o d e l i n g [Falkenhainer & Forbus, 1991] is an implemented technique for organising domain knowledge into model fragments to support large-scale multi-grain multi-perspective models.
A model fragment consists of four parts: structural configuration, relevance assumptions, operating conditions and relations imposed on parts of the model. These parts allow the re-usable model fragments to be
composed into larger models to solve specific problems, e.g. only model fragments with appropriate operating conditions can be used in a situation.
The relevance assumptions include grain-size, ontology (e.g. contained stuff, energy flow, molecular collection), approximations and abstractions. [Falkenhainer & Forbus, 1991] describes compositional modeling in terms of mechanical devices but considers the algorithms to be domain independent. The project’s ultimate aim is to produce a generic ITS and they implicitly consider the multi-grain multi-perspective problems to be endemic to all domains.
The essence of compositional modeling is selecting and combining elements of a large library of partial models in the most efficient and relevant way possible given the constraints imposed by the problem. The significance of the work is how much prominence is given to viewpoints (grain size and perspective) as a means of focusing computational effort. Others
Cyc [Guha & Lenat, 1990] is an AI project to develop a system with common sense by representing a very large body of knowledge.
CycL, the representation language of Cyc, allows a set of sentences to be declared a microtheory and have associated with it a description related to their scope and use. Microtheories allow Cyc to reason about grain size, multiple representations and, by restricting their context, simplify the axioms used.
KRL [Bobrow & Winograd, 1977] is a frame-based knowledge representation language that integrates procedural and declarative knowledge. One of the major intuitions that form the basis of KRL is:
A description [of a conceptual entity] must be able to represent partial knowledge about an entity and accommodate multiple descriptors which can describe the associated entity from different viewpoints.
[Bobrow & Winograd, 1977]
Other knowledge representation languages, such as KL-ONE (Brachman), ART (Williams), have provided similar capabilities for describing objects from different viewpoints ‘but no particular approach for applying them within a domain or using them for appropriate explanation’ [London, 1991] pp 42.
An alternative approach is that of representing Graphs of Models [Addanki, Cremonini & Penberthy, 1991] – models are treated as nodes and edges represent the assumptions that have to be changed in moving between the models. When a model conflicts with a problem the system switches to another model that is based on assumptions that tend to alleviate the conflict. Graphs of Models (or GoMs) have been implemented in physical domains where the typical assumptions may involve a frictionless system, rigidity or mass distribution. This approach highlights the problem of model choice – how to choose a new model when the present model is inadequate – and makes the relationships between the different models (viewpoints) particularly explicit.