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Intelligent agent behavior
based on organizational image
theory
David G. Schwartz and Dov Te'eni
Graduate School of Business Administration, Bar-Ilan University, Israel
Keywords Intelligence, Image, Logic, Decision making, Cybernetics, Management
Abstract Image theory has been used, in numerous studies, as a basis for understanding and
describing the decision-making activity of managers in both cooperative and competitive
environments. The fundamental division of duties prescribed by image theory ± namely adoption
decisions and progress decision ± maps very well to the adaptability requirements of intelligent
agents. The issues of adaptive planning and execution monitoring in agents can be well served by
applying the empirical lessons learned from the application of image theory across groups of
decision makers. This paper explores the concepts of adoption and progress decisions in the
context of image theory and provides a basis for creating image-theoretic agents. This paper sets
the foundation for an interdisciplinary bridge between Beach and Mitchell's Image Theory for
human decision making, and the construction of intelligent agents. We begin by presenting image
theory and describing its use among human decision makers. We then show how the mechanisms
of image theory can be implemented in an agent-based architecture to implement both execution
monitoring and adaptive planning. This is done through the image-theoretic constructs of
progress decisions and adoption decisions. We conclude by presenting logic-programming
implementation of the Imaginal Agent Architecture that supports the adaptive planning and
execution monitoring of agents through the use of meta-level constructs for adoption and
progress decisions.
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Vol. 30 No. 2, 2001, pp. 166-178.
# MCB University Press, 0368-492X
Introduction
A decision-making agent is an agent whose primary activities involve the
selection of suitable goals, the determination of plans through which those
goals can be achieved, and the implementation of such plans in the pursuit of
effective decision making. As found in many agent architectures, a decisionmaking agent must make use of some form of values (beliefs), goals (desires),
and plans (Georgeff and Lansky, 1987; Suchman, 1987; Shoham 1993). It is the
dynamic modification of those values, goals, and plans that will allow an agent
to exhibit adaptive characteristics. Agents exhibiting adaptive characteristics
are capable of dynamically adapting their problem-solving methodologies in
order to achieve superior performance in their decision-making tasks.
This paper presents a treatment of image theory (Beach and Mitchell, 1987,
1990, 1998) as a basis for creating adaptive intelligent agents. Image theory is a
theory of organizational behavior and managerial decision making that has
heretofore been applied exclusively to the modeling of human decision-making
activity. Previous work on image theory as a basis for agent architectures has
been limited to determining a set of agent design principles based on the
decision-making framework suggested by the theory (Schwartz and Te'eni,
1996). In contradistinction to de Raadt (1991), who proposed a cybernetic
approach to information systems and organizational learning, we are Intelligent agent
developing an organizational learning approach to elements of cybernetics.
behavior
Image theory has been used, in numerous studies, as a basis for
understanding and describing the decision-making activity of managers in
both cooperative and competitive environments. The fundamental division of
duties prescribed by image theory ± namely adoption decisions and progress
167
decision ± maps very well to the adaptability requirements of intelligent
agents. The issues of adaptive planning and execution monitoring in agents
can be well served by applying the empirical lessons learned from the
application of image theory among groups of decision makers. This paper
places particular emphasis on the concepts of adoption and progress decisions
in the context of image theory and provides a basis for creating image-theoretic
agents. We begin by presenting image theory and describing its use among
human decision makers. We then show how the mechanisms of image theory
can be implemented in an agent architecture to improve both adaptive planning
and execution monitoring. This is done through the image-theoretic constructs
of adoption decisions and progress decisions. We conclude by presenting logicprogramming implementation of the Imaginal Agent Architecture that
supports the adaptive planning and execution monitoring of agents through
the use of meta-level constructs for adoption and progress decisions.
Background and related work
Intelligent agents
Trying to present a consensus definition of intelligent agents has long been
abandoned, and rightly so. Russel and Norvig (1995) provide what is perhaps
the most general of accepted definitions, considering an intelligent agent to be
any entity that perceives its environment through sensors and effects its
environment through some form effector.
Agent architectures
Agent architectures have been approached from a number of different
perspectives ranging from game theoretic models (Rosenschein and Zlotkin,
1994) to biological models (Beer, 1990). In between we find the use of shared
mental models (Sycara and Lewis, 1991), and social interaction models (Gasser,
1991; Hewitt, 1991; Star, 1989; Werner, 1989). The adaptive nature of agents has
received considerable attention from the perspectives of traditional learning
and discovery, knowledge-based modification (Imam, 1996), and dynamic plan
generation (Pollack, 1992). Sedbrook (1994) draws parallel between dynamic
group processes and genetic learning.
Moore et al. (1998) propose a brokered agency architecture with four types of
agents:
(1) user agents to handle the UI and initial problem partitioning to one or
more broker agents;
(2) broker agents to divide and subcontract out different subproblems;
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(3) domain specialists who handle actual subproblem resolution; and
(4) database wrappers who deal with the integration, access, and update of
legacy data required by the other agents.
Classic adaptive agent algorithms address adaptability exclusively in terms of
changing the plan to be executed. The issues of adapting fundamental agent
behavior, tendencies, strategies, and priorities have taken a back seat to the
decision-theoretic approach. The application of organizational behavior and
managerial decision-making models has yet to receive thorough treatment in
an agent architecture.
Human decision makers
In selecting an appropriate theoretical basis for an agent architecture, it is
important to consider the performance characteristics of the theory. If the
theory is ``new'', i.e. with no basis in contemporary literature to support its
effectiveness, it becomes difficult, if not impossible, to justify the theory as a
basis for agent behavior. It is perhaps the difficulty of this task that has
resulted in many agent architectures lacking a firm theoretical basis.
Humans have been shown to adapt well to changing conditions (Payne,
1982). A series of simulations and laboratory experiments have shown that
adapative rules for adopting decision rules can describe decision behavior well,
and perhaps more importantly, that this behavior is efficient (Bettman et al.,
1990; Payne et al., 1988). In fact, it has been argued that some of the human
tendencies or biases that are often the basis for assuming suboptimal behavior
(e.g. Kaneman and Tversky, 1972) are human ways of adapting in a changing
environment (see Einhorn and Hogarth, 1981). For example, Ben-Bassat and
Te'eni (1984) simulated human heuristics, such as discounting unfamiliar
attributes, and found that human heuristics did no poorer than Bayesian
decision rules.
One of the main factors in the high quality of human decision making is the
adaptive nature of the human decision maker. We believe that by examining
the theoretical bases of effective human decision making, agent researchers can
build more robust decision agents with effective mechanisms for both
execution monitoring and adaptive behavior. This approach is consistent with
the directions taken by Suchman (1987) in her work on situated action, as well
as Georgeff and Lansky (1987), Rao and Georgeff (1993) and others.
Image theory
This paper focuses on a specific theoretical basis for human decision making
known as image theory. Image theory was developed by Lee Roy Beach and
Terence R. Mitchell in the late 1970s and has since served as a basis for
numerous empirical studies of managerial decision making (Dunegan, 1995;
Beach and Strom, 1989; Beach et al., 1988).
The following section explains image theory and presents its suitability as a
basis for adaptive agent architectures. This is followed by a discussion the
implementation of image theory and its main algorithmic components that Intelligent agent
produce the adaptive behavior of image-theoretic agents.
behavior
Image theory
Image theory has been developed along two complementary tracks: for
personal decisions (Beach and Mitchell, 1990, 1987); and for decisions within
organizations. Our primary concern is with the latter given its focus on
individuals acting as decision agents on behalf of organizations.
Types of images
In organizational image theory, a decision maker can be profiled in terms of his
or her organizational images. There are four such images, as summarized
below (Beach and Mitchell, 1990, 1998). Each type of image is a representation
of the decision maker's perception of some aspect of the organization.
(1) Organizational self-image. The organizational self-image consists of the
beliefs, morals, ethics, values, norms, etc. that are generally accepted
across the organization. These images are formed irrespective of the
individual's opinions and are meant to provide an accurate reflection of
the organization's principles. It is these principles that will provide the
basis upon which new goals are generated and candidate goals are
evaluated.
(2) Organizational trajectory image. The organizational trajectory image
contains the goals and goal markers that comprise the organization's
agenda for the future. These goals can be concrete events, abstract
states, or interim non-goal states (markers) that are milestones on the
path to a goal.
(3) Organizational action image. The organizational action image is made
up of a set of plans associated with each of the goals in the
organizational trajectory image. Each plan consists of tactics that
implement the specific behavior of a given plan.
(4) Organizational projected image. The organizational projected image is a
forecast of anticipated events and states that are expected as result of
implementation of the organizational action image.
Adoption and progress: fundamental activities for the adaptive agent
In image theory, the decision maker modifies his image on an ongoing basis.
This dynamic adaption is considered fundamental to the activity of human
decision makers.
Each of the self, trajectory, and action images are serviced by adoption
decisions whose purpose is to adopt or reject candidate goals and plans from
these three images. A second decision category, progress decisions, services the
organizational action image by determining which of its plans are progressing
in a satisfactory manner towards its goals.
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Adoption decisions
Adoption decisions determine whether to augment current goals and strategies
or use them as they are. Adoption decisions rely on two types of tests:
compatibility and profitability. The compatibility test is a yes/no test and is
non-compensatory. It tests the fit between a candidate decision policy and the
decision maker's set of images. A candidate is immediately adopted if it is the
only candidate that fits ± where fit is defined as being within some threshold
value to the attributes in the decision maker's images. If several candidates fit
the decision makers images, the profitability test is applied to select the best
candidate. The profitability test evaluates the potential pay-offs (both positive
and negative) of a given candidate goal with respect to alternative candidates.
There are many ways in which decision strategies can be compared, ranging
from the effortless random choice up to highly analytic procedures such as
maximum expected utility (see the cost-benefit model defined in Beach and
Mitchell (1987).
During the adoption phase, these two tests are applied in sequence, the
compatibility test filtering out unacceptable candidates, and the profitability
test selecting the best from the remaining compatible candidates.
One aspect of image theory's adoption decisions that is of significant interest
is its emphasis on the do-nothing reaction. The tendency of human decision
makers towards protecting the status quo has been shown to be an important
factor in the efficiency of human decision. This tendency, when mapped to an
adaptive agent architecture, can result in more efficient agent activity.
Progress decisions
Progress decisions monitor the processes underway to determine whether the
plans being implemented still fit the goals or require modification. They do this
by performing a comparison between the organizational trajectory image and
the organizational projected image. If there is a reasonable fit between the two
images, it is indicative that the organizational action image contains suitable
plans for achieving the trajectory image's goals. If the trajectory and projected
images are found to be incompatible, it is indicative of either unsuitable goals
in the trajectory image, inappropriate plans in the action image, or both. In
either case, it requires some form of adaptive response.
Image theory does not prescribe the exact procedure for generating the
adaptive response when events generate conditions that are not specified in the
current images. We suggest that the reflective nature of effective decision
makers should also be modeled into the image theory framework. Reflection is
needed when the current way of thinking breaks down and new components of
the images must be developed. Effective reflection is characterized by
systematic transition between levels of abstraction, between the activities of
examining current images and testing new ones, and between the different
parts of the problem (Srinivasan and Te'eni, 1995). In the image theory
framework, the levels of abstraction are given by the hierarchy of the images,
and the activities are shown in Figure 1. When the trajectory images cannot
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Figure 1.
Decision cycle of an
image theoretic agent
explain the unexpected results of action, and new plans must be developed,
they must be developed by consulting the higher levels of the self-image. But
developing new plans also means testing them by forecasting the results of
action ± a simulation, if you will.
In terms of intelligent agent architectures, progress decisions play a
fundamental role in providing execution monitoring. This execution
monitoring, together with the recursive application of adoption decisions to
modify both the trajectory and action images, provide a complete adaptive
planning mechanism.
Implementing the Imaginal Agent Architecture
Motivation
Implementation of the Imaginal Agent Architecture uses the Prolog language.
The choice of Prolog had two primary motivations:
(1) the clarity provided by the declarative representation of Logic
programming makes the mapping from Beach and Mitchell's loosely
algorithmic description of image theory more intuitive;
(2) the meta-level characteristics of Prolog provide an elegant and effective
framework for implementing the different layers of decision-making
activities that image theory demands of the imaginal agents.
Object and meta are relative terms. Two languages O and M, the object
language and meta language respectively, are in an object-meta relationship if
there is a representation of the language O in M. Meta-programs are programs
written in meta-language which manipulate and/or reason about an object
language. The object-meta relationship is of particular interest in the design of
adaptive agents in that it provides an opportunity for reflection ± jumping
between the object and meta language levels. The reader is referred to
(Schwartz, 1995) for a discussion of the use of Prolog and meta-interpreters as a
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basis for agent architectures. In the interest of brevity, we have shown only the
primary predicates used in implementing the theory.
In the implementation of the imaginal agent architecture, the image itself is
considered a component of the object level and not of the meta level as one may
expect. The reason for this becomes clear when you consider that the actual
images are agent-specific and are tightly bound to the specific goals and plans
that an agent must execute. It is the monitoring and modification of these
images that takes place at the meta-level.
Mapping the theory to agent constructs and behaviors
Figure 2 presents the knowledge-base constructs that comprise the object-level
of an image-theoretic agent. Here we find representation of each of the image
components required by the decision maker. The aggregate agent_image/4
predicate is used as a container for the four organizational images.
The plan/1 predicate is a surrogate for any number of possible
implementations of a plan representation. This is another advantage of the
meta-interpreter approach ± we can rely on Prolog's clause resolution
mechanism to provide the meta-level with goal-size pieces of the planning
predicates, irrespective of their complexity. In other words, the generality of
meta-interpretation lets us avoid a prescriptive plan representation and allows
different types of agents to use their own plan representation.
The image-theoretic meta-interpreter appears in the Appendix. The metainterpreter takes the agent's action image as input and proceeds recursively
down the plan elements in the action image. As each planning step is
implemented, the meta-interpreter detects new events. These events can be
both internally and externally generated. For each step in plan implementation,
the image_theory/3 predicate is invoked with the current context, current
image representations, and current event list.
Figure 2.
Image theoretic
knowledge-base
constructs
If a suitable policy is found for an equivalent context, then this policy's actions Intelligent agent
are performed. If no suitable context was found, we then forecast the result of
behavior
doing nothing. If the resulting status quo is compatible with the decision
maker's images, then no action is taken and the event passes with no response.
The forecast/4 predicate is used to project the results of an event occurring in
the current context with no action being taken on the part of the agent. The
173
compatible/2 predicate applies a compatibility test of the forecast results with
the current image.
Execution monitoring takes the form of ongoing comparisons between the
organizational trajectory image and the organizational projected image ± the
progress decisions. If the forecast of a do-nothing decision results in the
creation of a trajectory image that is incompatible with the projected image,
then intervention is required. It is here that adaptive behavior begins to take
place ± the adoption decisions. After intervention, the new goals and plans are
integrated with the existing image to form a new image which is used in future
goal processing. The modification of the decision maker's images will have a
direct effect on future progress decisions through the compatibility test, thus
adapting the agent to new circumstances and changing its projected future
reactions. The decision cycle, as illustrated in Figure 1, continues until the
action image is devoid of plans.
Future directions
Modeling decision scenarios
Following completion of the Prolog implementation, the Imaginal Agent
Architecture must be tested in a variety of decision scenarios. One such
scenario, the hiring decision, described in terms of image theory constructs
by (Schwartz and Te'eni, 1996), is currently under implementation. Another
candidate scenario is the project management decision presented in
(Dunegan, 1995). This example is of particular interest as it has been applied
in two different empirical experiments testing the use of image theory by
human decision makers. Dunegan's results provide us with an interesting
benchmark with which to evaluate the performance of image-theoretic
agents.
Examining group decision making
In this paper we have examined image theory from the perspective of an
individual decision maker within an organization. This was done with the
intent to focus our adaptive agent architecture on the functions and
requirements of individual image-theoretic agents. Extending this discussion
from the individual decision maker to group decision making within an
organization is a natural next step.
Examining additional theory-based decision models
The management and decision science literature is an excellent source of
other models that are of interest when designing adaptive agents. Of
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particular interest is the Vroom and Yetton Model of Leadership (1973).
This model is interesting as it places great emphasis on the decision maker
switching between different decision-making styles. Vroom and Yetton
define five decision models with accompanying metrics for switching
between models. The ability to switch between decision models is both a
strong indication of adaptability and an excellent match for the meta-level
architecture. We are currently considering the Model of Leadership theory
as a possible augmentation for image theory in moving from individual
decision to group decisions, given the Model of Leadership's emphasis on
multiple participants. We believe that further investigations into the
application of managerial decision models to the design of intelligent
agents can both benefit from, and contribute to, the two disparate
disciplines.
Conclusions
In this paper we have presented the use of Beach and Mitchell's image theory
as a basis for building adaptive intelligent agents. The resulting architecture,
called the Imaginal Agent Architecture, is a framework that is built around a
hierarchy of images and the activity at the different levels of the hierarchy. The
Prolog implementation shows at a general level the feasibility of building such
an architecture. Implementing image theory as a meta-interpreter provides us
with a framework in which to experiment with the different progress and
adoption tests that can be developed for different agents, without requiring any
change to the underlying image-evaluation mechanism. Our main goal in
presenting this framework has been to set the foundation for an
interdisciplinary bridge between management decision theory and the
construction of intelligent agents.
The adaptive nature of human decision behavior seems to have potential use
in the design of intelligent agents. Effective decision behavior rests on being
adaptive in both decision strategy adoption and in decision strategy
implementation. Image theory provides the theoretical basis for explaining
adaptive decision behavior by employing both progress decisions on the
implementation side and adoption decisions on the decision strategy side. We
extend image theory to include reflective human heuristics for progress
decisions. Moreover, the human decision maker's tendency to do nothing, i.e.
maintain the status quo, finds expression within image theory and in the
consequent imaginal agent architecture. This paper attempted to specify the
processes of decision behavior according to the extended image theory, in order
to come closer to an architecture of adaptive intelligent agents that is consistent
with the theory.
The implementation of managerial decision scenarios, as well as the
extension of the Imaginal Agent Architecture to support group decision
making, provide ample interesting directions for the continued exploration of
image-theoretic agents.
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Appendix
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