=Paper=
{{Paper
|id=Vol-133/paper-8
|storemode=property
|title=Placing Newly-Arising Goals in the Proper Context
|pdfUrl=https://ceur-ws.org/Vol-133/CSGC_No07_Turner.pdf
|volume=Vol-133
|authors=E. Turner,R. Turner,E. Albert
}}
==Placing Newly-Arising Goals in the Proper Context==
https://ceur-ws.org/Vol-133/CSGC_No07_Turner.pdf
Placing Newly-Arising Goals in the
Proper Context∗
E.H. Turner, R.M. Turner & E. Albert
Department of Computer Science
University of Maine
Orono, ME 04469–5752
eht@umcs.maine.edu
Phone: +1 207 581–3943 FAX: +1 207 581–4977
Keywords: Collaboration, focus of attention, context, conversation control,
autonomous agents, multiagent systems.
Abstract
When agents collaborate in the real world, they must be able to handle new
goals that arise as they are executing a plan. If we can predict how goals
will be satisfied, we can predict the context in which actions will be executed.
When new goals arise, we can place them in the predicted context where they
will be most effectively achieved. In this paper, we first discuss JUDIS, an
implemented system which places newly-arising goals into predicted discourse
contexts. Next, we discuss how the approach used in JUDIS is being extended
to more general problem solving.
1 Introduction
For independent agents and agents working in multi-agent systems alike, the
context in which an action is performed often determines the success of that
∗
The authors would like to thank the United States Office of Naval Research for its sup-
port of this work under grant number N00014–01–1–0818. The content does not necessarily
reflect the position or the policy of the U.S. government, and no official endorsement should
be inferred. The authors can be reached via e-mail at eht@umcs.maine.edu. For further
information, see MaineSAIL.umcs.maine.edu.
1
action. In conversation, it is essential that the proper utterances are placed in
the proper context. In addition to allowing those utterances to be interpreted
properly, grouping utterances by topic allows the conversation to be coherent.
In other kinds of collaborative work, placing actions in the proper context is
also important. By grouping actions together with others that require the
same important features of a context, the actions can be achieved efficiently.
Also, agents can be more helpful if they recognize another’s action as being in
the context of a larger plan. However, since in the real world new goals may
arise after an agent has begun to execute its plan, agents must be able to place
goals in the proper context as they arise.
In this paper, we present our approach to placing actions within the proper
context. This includes focusing attention on the proper action to be executed
next. Actions that can be used to achieve an agent’s current goals are grouped
together by important, predictable features of the context. At the time that
the next action must be chosen, potential actions are evaluated based on how
well they fit into the current context and the priority of the goal that they will
be used to achieve.
We begin in Section 1 with a discussion of JUDIS [1], a dialogue system
in which the approach was first implemented to create coherent conversations
between a multi-agent system and a user. In Section 2, we discuss our plans for
extending this approach to general problem solving, as well as conversation,
in collaborative agents.
2 Fitting Goals into Conversation Contexts
The approach described in this paper was first implemented in JUDIS [1].
JUDIS controls conversation for a distributed problem solver in the domain
of menu planning [2]. As each problem solver works, goals might arise to
get information from or to give information to the user. These goals can be
achieved by adding utterances in the conversation. However, because different
problem solvers may be working on different parts of the menu at the same
time, the utterances cannot simply be included in the conversation in the
order in which they arise. Instead, JUDIS must find the proper context for
the utterance. In JUDIS we are concerned only with using the context to
make the conversation coherent.1 Consequently, the context of the current
1
Using the context to interpret utterances is beyond the scope of this work.
2
discourse can be represented by the current topic.
To group utterances together, JUDIS uses a template for conversation.
The template is built from conversation schemas represented using Conversa-
tion MOPs [3, 4]. Initially, the template is a very general structure, predicting
the opening and closing of the conversation in detail and merely the expecta-
tion that there will be a middle that will be used to discuss a meal. At the
end of the opening, JUDIS asks the user for information about the meal that
is being planned. As goals arise from problem solvers2 JUDIS adds detail to
the middle of the conversation.
In JUDIS , utterances are added to the template through a conversation
schema that can include both goals. For example, consider a situation in which
a traditional planner working on the appetizer wants to suggest gazpacho and
sends this goal to JUDIS . Assume this is the first goal. JUDIS knows that
the discussion of the meal will include a discussion of the appetizer. It adds
this goal to the appetizer topic. If no other goal were to enter the system
before JUDIS began discussion of the appetizer, it would simply tell the user
that it would recommend gazpacho.
Now suppose, that at the same time that the planner is working on the
appetizer, a case-based reasoner is working on the main course. It is trying to
apply a case in which someone became sick from the main course of stuffed
peppers because he was allergic to green peppers. The case-based reasoner
will only suggest this main course if it knows that it can avoid the failure of
the remembered case, so it sends a goal to JUDIS to find out if anyone is
allergic to green peppers. It will not ask JUDIS to suggest stuffed peppers
until it knows the failure can be avoided. Assume that this goal has arisen
just after JUDIS has switched the topic to the appetizer, but before making
the suggestion of gazpacho. To add the question about green peppers, JUDIS
extends the topic of the appetizer to include a discussion of the ingredients in
the appetizer. This is done using the conversation schema for a list.
Now, suppose the planner is trying to determine if the user would like to
include parsley in the gazpacho. It creates a goal to ask the user about parsley.
Since parsley is an ingredient of gazpacho, the utterance will be included as
2
Due to the delayed implementation of the distributed system, JUDIS worked with the
problem solvers on only small problems. To test JUDIS for more complex streams of goals,
streams of conversation goals from each problem solver and streams of goals constructed
following the problem solving approach of the problems solvers were used.
3
part of the list already in the template.
The conversation is being conducted as new goals arise and must be added
to the template. To help JUDIS participate in the on-going conversation
without frustrating the user, the template always represents a coherent con-
versation. However, the order in which utterances will be said is not completely
determined by the template, so JUDIS cannot simply focus attention on the
next utterance in the template to select the next utterance. There are two
main reasons for this. First, the schemas themselves do not completely de-
termine which utterances will be said when. Instead, conversation schemas
often provide only a partial ordering for their scenes. They also have optional
scenes which may or may not be included in the conversation. Second, there
may be reasons why a schema might be violated. An urgent utterance, such
as warning of an immediate safety hazard, may cause a speaker to abandon a
conversation schema entirely. There are also cases where speakers must move
back to previous topics to incorporate goals that were not known when those
topics were originally covered. In some cases, a speaker may need to move
ahead in the conversation to handle a high priority goal quickly.
In JUDIS , we see the problem as a tension between convention, as rep-
resented by the schemas in the template, and intention, as represented by the
priorities of the active goals. JUDIS combines these influences in the form
of activation sent to utterances in the template. If the utterance is ready to
be said based on conventions of conversation, it receives a fixed amount of
activation. An utterance is ready to be said if two conditions are met. First,
the schema which contains the next utterance must also contain the previ-
ous utterance, or the schema which contains the previous utterance must have
completed and the schema which contains the next utterance must be ready for
execution. Second, all mandatory schemas and utterances which must precede
the utterance must have been executed.
The activation based on intention is given to utterances depending on the
priorities of the goals which they are meant to achieve. For very high priority
goals, the priority of the goal will outweigh the template and the utterance
will be said immediately. In less extreme cases, the priority of the utterance
will help choose between utterances that are ready to be said in a schema
with partially-ordered steps. Intention-based activation also allows optional
utterances in schemas to be included in the conversation. The details of our
method of passing activation is discussed in more detail in [5].
4
3 Exploiting Context to Focus Attention in
General Problem Solving
We are beginning to design a system which will take the approach to focusing
attention used in JUDIS and apply it to general problem solving. The system
will work in the domain of Autonomous Oceanographic Sampling Networks
(ASONs) [6]. In AOSNs, underwater vehicles and non-mobile sensor platforms
collaborate to collect data for scientific missions. Agents in the AOSN are
given a mission by a scientist or group of scientists. New goals can arise, for
example, in response to unexpected events in the environment or as scientists
make new requests. As in JUDIS, we fit the actions required to satisfy the
new goals into an appropriate context created by executing the existing plan.
Contexts are too rich for us to try to match potential contexts expected in
the plan with the perfect context for executing a new action. Instead, we must
group actions together using only particular features of the context. From our
domain-dependent work with JUDIS , we have learned that two characteristics
should apply when deciding the features of the context that should be used
for the grouping. Goals should be grouped together by features of the context
that are costly to acquire and which can be predicted with a great deal of
certainty. When we apply this principle to problem solving in AOSNs, we
group actions together if they take place at the same location or if they use
the same resources that are costly to acquire. We call these resources, high-cost
resources.
For general problem solving, there is some difficulty in predicting the new
actions that must be fit into the plan. In JUDIS, the problem solvers’ goals to
get or to give information were easily associated with individual utterances.3
For the general problem solver, hierarchical schemas are used to store plans
that can be used to achieve goals (similar to those in Orca [7]). Important
features of the context can be stored in a generalization of plans to achieve the
goal. For example, if an agent had a plan to acquire a book, the most general
schema could indicate that the agent needs to be at a library or a bookstore
to get the book.
In JUDIS, the new actions were added to the template by linking them
through a detailed discourse schema. This is because we were concerned with
3
Of course, there are times when an utterance can satisfy several goals or when a single
goal can be satisfied with several utterances. JUDIS did not handle these cases.
5
conversation structure being followed to create a coherent conversation. How-
ever, it is time consuming both to add this much detail when a new goal
becomes active and to change the plan if it fails. For these reasons, and be-
cause plan coherence is less important in general problem solving, we simply
create nodes for high-cost resources and for locations.
When a new goal arises, it is added to the agenda. When the problem
solver focuses attention on the goal, it finds a schema which can be used to
satisfy that goal and places the actions of the schema on the agenda. If the
schema has information about the need for a high-cost resource or for the
vehicle to be at a specific location, that information can be used to help the
problem solver fit actions into the proper context. So, for example, when the
goal to read a book is replaced on the agenda by the schema to acquire a
book, the problem solver has the information that the agent must be either at
a bookstore or a library. If both nodes exist, the schema for acquiring a book
at the given location can be linked to the nodes. If neither exist, new nodes
can be formed to represent the library or the local bookstore. However, if one
node exists, the problem solver uses the prediction that the agent will be in
the proper context for one of the schemas. For example, if the agent will be at
the library for a meeting, it will be in the proper context for acquiring a book
at the library. In addition to being linked to these organizing nodes, the act
remains linked to the schema on the agenda.
There are several details that must be worked out for real-world planning.
For example, we must make sure that preconditions for any of the actions
associated with the node are not violated by the others. For example, an after
hours meeting at the library would not provide the proper context for getting
a book. We also need to decide how to determine that actions can be removed
from a node because they have been satisfied elsewhere.
We, again, use an activation model to focus attention on an action on
the agenda. Activation comes from three sources. The priority of the goal
contributes to the activation of each action that will achieve it. There are also
two kinds of context which contribute to activation: the schema’s connection
to features in the predicted context through the organizational nodes and the
context that the schema provides through the schema that it is part of. The
last is similar to the JUDIS’s convention-based activation and is meant, in
part, to help other agent’s understand the acting agent’s current plan. It
is especially important for agent’s to follow their schemas when engaging in
6
conversation. As we work out the details of activation, we will take this into
account. Activation can also be modified by contextual schemas. This is done
for goal priorities in Orca and will be extended to other forms of activation
later in our work.
4 Conclusion
We are developing an approach to placing newly-arising goals in context and
focusing attention on those goals as an agent executes its plans. The approach
was implemented to be part of a user interface to a distributed problem solving
system. We are currently extending this approach for more general problem
solving in a multi-agent system.
7
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