ORCA

ORCA - (the ORganizational Change Advisor) is a job aid and creativity tool developed at the Institute for the Learning Sciences at Northwestern University to assist consultants whose task is process redesign and managing organizational change. Using ORCA, the consultant systematically and iteratively analyzes the situation of a client in order to recognize potential business or organizational problems. The system assists the consultant in thinking and problem solving by retrieving historical cases that are judged relevant to understanding the client's situation. The retrieval and examination of past cases provides general perspective and contextual advice which helps the consultant plan for change and avoid past failures.

ORCA, the ORganizational Change Advisor is designed to teach consultants how to identify problems that may face a business, and to expose them to potential solutions to those problems. To do this, ORCA presents business war stories about organizational change in response to economic and technological pressures. As the user works with a client, the client becomes a new story in the system, thus extending ORCA to serve as a corporate memory.

As with ORCA, the intelligent tutoring agent's task is to construct a sufficient description of the player such that relevant stories can be retrieved. ORCA must elicit this description from a novice who presumably does not know the domain as an expert would. Consequently, the system must do everything it can to guide the user in describing the client. This guidance takes three main forms in ORCA:

  1. ORCA queries the user in terms of a vocabulary of features that are likely to be familiar. While ORCA hypothesizes about abstract thematic categories, the user is never directly confronted with them. Instead, categories are confirmed by asking questions that are specific to the business domain.
  2. The featural description of the client is elaborated by asking the user a series of questions, rather than permitting unrestricted text input or a large form-filling interface. Each question can be answered through buttons labelled "Yes", "Probably", "Maybe" "No", or "Don't Know". This question-asking interface is sometimes called a sounding-board (Kass 1991).
  3. To make it easier for the user to decide whether a feature applies to the client, the sounding board is augmented with an interface that lets the user compare and contrast the client with the stories just seen. For example, rather than deliberating about whether the nature of work in a company is "heavily dependant on information" it may be easier to decide if it is more or less so than for another company. This can be viewed as case-based elaboration of the problem description (Slator & Bareiss, 1992).

Once a client problem is sufficiently described, the system retrieves and presents an analogous story to the user. These stories are embedded in a multimedia ASK Network (Ferguson et al, 1992) that allows the user to view the story and to browse through related stories by traversing relational links. For this project, we anticipate a similar mechanism for agent-player interaction. The agent will visit the player, and share case-based experiences with the player. A follow-up dialog similar to the one described above will also be employed. However, the mechanism for initial case retrieval will be much different.

The principal case retrieval mechanism in ORCA is reminding, based on association among features. This mechanism can be characterized in terms of the following design decisions:

  1. The basic retrieval mechanism is a simple, single-step spreading activation in an associational network. When a question is answered, the feature corresponding to the question propagates activation to its nearest neighbors. The next most active feature is then chosen and presented to the user as a question.
  2. Features may be confirmed at different levels of activation, and links between features have different strengths. When the user answers a question through one of the five reply buttons, the corresponding feature is confirmed at the specified activation level. For example, "Yes" confirms a feature with activation = 4, "Probably" confirms a feature with activation = 2, and so forth. When activation is propagated to neighboring features, it is attenuated by the strength of the link.
  3. The network is homogeneous. All links are essentially reminding links with different strengths. The idea is that if one feature is associated with the client, then it should remind the system of other features that are also likely to be relevant. The reminding links between features can be thought of as a feed-forward network that helps the system to ask relevant questions to elaborate the problem description.
  4. All reminding links are bi-directional. While, conceptually, reminding links might be asymmetric (e.g., dogs remind you of fur, but not vice-versa), in working towards a simple representation, we deemed it impractical for the the system builders to make these judgements.
  5. Features are propositional. In order to be compatible with spreading activation, features are represented as simple propositions, rather than as predicates, or attribute-value pairs.
  6. Features are subdivided into two types. Surface features represent directly observable properties of the client. Abstract categories represent deep thematic problem situations.

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e-mail: slator@badlands.nodak.edu