=Paper=
{{Paper
|id=Vol-1420/wois-paper2
|storemode=property
|title=Online Communities for Agent Collaboration in Cyber-Physical-Social Systems
|pdfUrl=https://ceur-ws.org/Vol-1420/wois-paper2.pdf
|volume=Vol-1420
|dblpUrl=https://dblp.org/rec/conf/bis/SmirnovLS15
}}
==Online Communities for Agent Collaboration in Cyber-Physical-Social Systems==
https://ceur-ws.org/Vol-1420/wois-paper2.pdf
Online Communities for Agent Collaboration in
Cyber-Physical-Social Systems
Alexander Smirnov1,2, Tatiana Levashova1 and Nikolay Shilov1,2
1
St.Petersburg Institute for Informatics and Automation of the Russian Academy of Sciences,
St.Petersburg, Russia
2
ITMO University, St. Petersburg, Russia
{smir, tatiana.levashova, nick}iias.spb.su
Abstract. The paper focuses on resource collaboration in cyber-physical-social
systems. Technologies of ontologies, intelligent agents, and online communities
are used to enable interoperability of human and non-human resources. An
agent ontology and major principles of agent collaboration are proposed. The
proposed ontology is based on the earlier developed ontology for resource self-
organization. That ontology is specialised for agent collaboration empowered
by online communities. The examples from smart room domain and smart
travelling domain are concerned with scenarios of agent collaboration.
Keywords: cyber-physical-social systems, agent collaboration, online
communities, ontology, context, smart space
1 Introduction
Cyber-Physical-Social Systems (CPSSs) are a new generation of networked systems,
wherein humans are an integral part. This is enabled to a considerable degree by the
fact that networked computers are everywhere, not only in the form of personal
computers but also in the form of cell phones, tablets, smart appliances, etc. The
benefit of CPSSs is twofold. On the one hand, while cyber-physical systems provide
computation facility for personal usage, CPSSs offer computation facility for social
use. On the other hand, in the CPSSs, humans are not only service consumers, but
"collaborators" as well. Humans may provide data, process data, make decisions, and
act on the data outputs. Integration of social resources into technical systems may
improve the systems in a number of directions. For instance, humans may contribute
to increase intelligence of the systems, situation awareness, system scalability, etc.
The tight combination and coordination between systems' computational, physical,
and social elements make CPSSs different from other forms of systems. The paradigm
of CPSSs involves networks of people (social networks), intelligent devices, and
mobile personal computing and communication devices, which form CPSSs [1]. The
necessity in integration different networked technologies such as computing
networks, sensor networks, and social networks attracted significant interest of
researchers from social science, computer science, computer engineering, electronic
Copyright © 2015 by the authors. Copying permitted for private and academic purposes.
This volume is published and copyrighted by its editors.
124
engineering, etc. The mentioned technologies deal with different kinds of resources
from physical, cyber, and social worlds. Enabling these heterogeneous resources to be
interoperable is essential for CPSSs.
The first thing to attain resource interoperability is a common context
understanding by resources. In this direction, the Semantic Web proposes ontologies
as the key technology to allow heterogeneous objects come to the same meaning. The
second thing for resource interoperability is the resource capabilities to communicate.
CPSSs comprise human and non-human resources. Technology of intelligent agents is
a good solution to provide the non-human resources with communication capabilities.
At present, online communications become common human practice. In the paper this
practice is proposed to be applied to agent communication.
The paper proposes an agent ontology and major principles of agent collaboration.
The proposed ontology is based on the earlier developed ontology for self-
organization of resources of CPSSs [2, 3]. In this paper the ontology is specialised for
agent collaboration empowered by online communities.
The rest of the paper is organized as follows. Section 2 offers the ontology of
CPSS and its specialization for agent collaboration. Section 3 postulates major
principles of agent collaboration in CPSSs. Scenarios of agent collaboration are
considered in Section 4. Main concluding remarks are given in Conclusion.
2 Cyber-Physical-Social System
A CPSS consists of cyber space, physical space, and mental space [4]. These spaces
are represented by sets of resources. The physical space consists of various
interacting information and computational physical devices. These devices united on
the communication basis organize the cyber space. The mental space is represented by
humans with their knowledge, mental capabilities, and sociocultural elements.
All the three spaces are tightly related. Information from cyberspace interacts with
physical space (physical devices) and mental space (humans). In this research the
interaction between these spaces is organized through online communities.
Due to complexity of CPSSs, differences in the operation of cyber, physical, and
mental components, and significant interdependencies among these components,
software agents are seemed a promising technology to model interactions between the
spaces [5, adapted]. Agents are autonomous and intelligent objects. It is proposed to
provide each resource with an agent. The agent invokes the resource's services,
interacts with other agents, and models the resource's behaviour.
Context determines the purpose of resource interactions. The understanding of the
context by agents is achieved by using a common ontology for context modelling.
2.1 Ontology
According to [6], any information describing an entity’s context falls into one of five
categories for context information: individuality, activity, location, time, and
relations. The individuality category contains properties and attributes describing the
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entity itself. The category activity covers all tasks this entity may be involved in. The
context categories location and time provide the spatio-temporal coordinates of the
respective entity. Finally, the relations category represents information about any
possible relation the entity may establish with another entity.
Ontologies serve to model context by ontologies’ means. Usually, such ontologies
consist of the upper ontology for general concepts, and domain specific ontologies
representing knowledge of different application domains [7, 8, 9]. The upper ontology
is shared by these domains. As a rule, the upper ontology represents concepts that are
common for all context-aware applications (Context Entity, Time, Location, Person,
Agent, Activity, etc.) and provide flexible extensibility to add specific concepts in
different application domains (i.e., Cell Phone can be a subclass of Device).
The proposed ontology for CPSSs has been built by experts based on the context
categorization above and contextual ontologies. The main requirement to the ontology
was to take into account the specifics of the social component. Most of the upper-
level concepts of the developed ontology (Fig. 1) correspond to the categories used to
describe entities' contexts. At this level, resources are thought of as the entities whose
contexts are to be described. Relations are represented by ontology relationships.
1 1 Upper ontology
Location Time
defines
defines
1
Context defines
describes has
1 1
Resource causes Event
is a is a
2 2
Physical device interacts Human
fulfils fulfils
1
provides Role
performs
1 consumes 1
Service Activity
is a is a is a is a is a
is a
Device Service Activity Person Role Event
typification typification typification typification typification typification
Domain ontology
is a and has relationships are 1:∞
arities of other relationships are undefined
Fig. 1. Upper ontology for cyber-physical-social systems
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Based on the analysis of various context ontologies (e.g., [10, 11, 12]) the experts
introduced in the ontology two more categories: service and role. The category
"service" represents resource functionality. Role is a position of a resource according
to which the resource performs some activity. The specialization of resources as
human and non-human (physical devices) was introduced in the ontology to consider
social constituent of CPSSs.
In the ontology (Fig. 1), resource's context is defined by location, time, resource
individuality, and event. Resources perform some activity according to the roles they
fulfil in the current context and depending on the type of event. At the same time, the
type of activity that a resource performs causes a type of event. For example, the
event of a phone call defines the human activity as answer the phone. But, when a
person raises the hand at the lecture time, this activity causes an event as, for instance,
lecture interruption. This explains bidirectionality of 'causes' relationship between
event and activity. The resources have some functionality in result of which they
provide services. The services provided by one resource are consumed by other ones.
In Fig. 1, upper indices in the boxes representing the ontology concepts indicate
the taxonomical level of these concepts. All the concepts of the upper ontology are
intended to be specialised in the application domains.
Common context represents the current situation in a CPSS. It is made up of the
contexts of the resources. The common context is the basis for agent interactions. The
purpose of these interactions is providing resources' services on demand. An agent
ontology is proposed below.
2.2 Agent
Fig. 2 represents the agent's ontology, which is based on the upper ontology above.
The concepts of the upper ontology are greyed. The concepts defined in the agent's
ontology are expected to be specified in particular application domains. The main
concepts of the ontology are described below.
Agent is an autonomous software entity that can either alone or working with other
agents, provide services on demand. Agent is used to represent CPSS’ resources of
both types: physical devices and humans. Agent is capable to make requests to
resources and provide their services.
Agent is described by profile. The agent's profile is represented by means of the
agent’s internal ontology and in a way understandable by other agents of the CPSS.
The internal ontology harmonises with the common ontology of the application
domain. The profile represents agent's properties (name, language, roles, preferences,
strategies, etc.) and the services this agent provides. The set of services defines the
agent’s functionality or a set of cyber-physical-social functions the agent can perform.
Through functionality the agent can change the common context.
Preference is an agent’s attitude towards a set of own and/or CPSS’ states and/or
against other states. The preferences influence the agent’s behaviour in the CPSS.
Behaviour is the agent’s capability to perform certain actions according to its role
in order to provide services. The behaviour is defined directly by the agent’s
preferences and indirectly by the strategy.
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Resource uses
is a is a
Physical device Human
is a is a
Internal
has Agent provides
ontology
fulfils has
represents
Profile Service
specialises Role represents
is a is a describes defines
Role in online Role in
community CPSS Strategy Preference Functionality
joins performs
Online Activity
defines
community
has
is a influences
Negotiation
Culture
protocol
defines
Norms conforms
govern Behaviour
Way of
communication causes
influences
updates Event
Context changes
updates
Time Location
Fig. 2. Agent ontology
Strategy is a pre-defined model of agent organization to provide services (e.g.,
interest groups, hierarchical, etc.). The strategy defines the negotiation protocol.
Agents negotiate to provide CPSS' services. A distinguishing feature of any CPSS
is that humans in these systems are not only consumers of services provided by the
CPSS but also services providers. This means the agents representing the both types
of resources participate in the negotiations. Online communities are used for the agent
negotiations.
128
Online community is a virtual community whose members interact with each other
via the Internet. Online communities are characterized by communication type
(synchronous/asynchronous); interests (universal, single-purpose group, event-based
group, etc.); supporting technologies (video, voice, messaging, etc.); culture; and
other properties. Recently, cultures of Internet-based communities have received
much attention [13]. In this paper online communities are characterized from this
point of view. Culture supposes language used to communicate and some norms
which govern the way of agent communications in the online community.
Communities may differentiate in their cultures.
Norms are communication patterns or information exchange patterns (e.g., direct
reciprocity, indirect reciprocity, preferential attachment [14]) specific for a particular
online community.
Way of communication is the pattern that the agent uses to communicate with other
agents and negotiate with them in an online community. The agents' preferences may
influence on the way of communication if the agents do not violate community
norms. Agent can join a community with different roles.
Role is a position of an agent according to which the agent behaves. Roles an agent
fulfils in online communities (e.g., visitor, novice, regular, etc.) differentiate from the
roles this agent fulfils in the CPSS (e.g., information resource, manager, decision
maker, executive, etc.).
The agents' internal ontology represents problems the agent is capable to solve.
Context specialises problems that the agent has to solve in the current situation.
Events happening in the CPSS and produced as results of agents behaviour cause
changes in context, the context is updated accordingly.
Negotiation protocol is a set of basic rules to implement the pre-defined agent
organization strategy. The main protocols include voting, bargaining, auctions,
general equilibrium market mechanisms, coalition games, and constraint networks.
The protocol conforms to the norms accepted in online communities.
3 Agent Collaboration
This Section presents principals of agent collaboration. In the CPSS the agents
communicate for two main purposes: 1) they establish links and exchange information
for better situation awareness; and 2) they negotiate and make agreements for
coordination of their activities for a proposed solution.
As it is said above, the agents communicate using a protocol able to implement the
strategy of agent organisation. In this research, such a strategy is agent collaboration
to provide services on demand. The following major principles of collaboration are
used as the basis for agent organization:
1. Contribution: the agents have to cooperate with each other to make the best
contribution into the overall system's benefit – not into the agents' own benefits.
2. Task performance: the main goal is to complete the task performance – not to get
profit out of it.
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3. Non-mediated interaction: the agents operate in a decentralized community and in
most of the negotiation processes there are no agents managing the negotiation
process and making a final decision.
4. Common terms: since the agents work in the same system they use common terms
for negotiation. This is achieved via usage of the common shared ontology.
5. Trust: since the agents work in the same system they can completely trust each
other (the agents do not have to verify information received from other agents).
6. Conformity: agents' way of communications conforms to the norms accepted in
online communities these agents join.
As any negotiation protocol requires an objective function to operate, it is proposed
to use “utility” of the solution taking into account preferences of an agent as the
objective function. The utility characterizes the “usefulness” of the solution for a role.
This utility can be calculated as a weighted sum of utilities of various activities
including into the solution.
Below, two scenarios of agent collaboration are considered.
4 Use Cases
The scenarios proposed in this Section illustrate different aspects of agent
collaboration. The first scenario focuses on domain-oriented ontology specialization
and agent communications in different online communities. The second one gives an
idea of the communication scenario in the part of “utility” of the solution.
4.1 Smart meeting room
The application domain considered in this Section is smart meeting room, which is a
kind of CPSS. The following scenario is treated.
In the meeting room a plan of business development is discussing. The real-time
data for the spreadsheets used in the business plan come from various corporative
information resources and projected in the screen. A meeting participant suggests
refining an aggregate function. For this some additional data are needed. No
resources available in the meeting room can provide the required data. The agents
find a consultant who provides the missing data.
Fig. 3 presents partly the specialization of the agent ontology for the scenario in
question. In the given part, concepts relevant to the scenario are presented only; some
relationships are left out for a better readability. The scenario involves humans
fulfilling three roles: meeting leader, meeting participants (these two roles are
common for meetings), and consultant. Agents responsible for interactions with
computational or information resources other than humans (shortly, IR agents) fulfil
the role of information resource. Interactions here mean requesting the resource and
providing its services. The person planning to fulfil the role of meeting leader
registers in advance to this role and information about the role is read from the profile
of this person.
130
fulfils
fulfils
fulfils has
Agent Profile
fulfils
Role represents
Servicer is a is a
is a Information resource
Role in online Role in CPSS
Meeting community Meeting leader
member performs
is a is a
joins Meeting participant
Activity
Consultant fulfils
Meeting room is a Online is a Online
community community consultant
has
Organization is a
Culture uses
culture
defines
Indirect is a
Norms
reciprocity
govern
uses
Instant Way of is a Asynchronous
messaging is a communication messaging
Fig. 3. Agent ontology for smart meeting room (a part)
In the scenario the agents firstly request the computational and information
resources available in the meeting room for the needed data. When the agents find out
that the required data are unavailable, they send online messages to the meeting
participants. The agents communicate via the meeting room community that is a kind
of online community.
The meeting room community comprises agents available in the meeting room. IR
agents and humans are joined to the community automatically. IR agents are assigned
the role of servicers. Humans are joined when they enter the room. In the community
they are assigned the role of meeting members.
The meeting room community accepts the organization culture. According to
norms this culture defines, contribution to an individual stimulates greater general
contribution to the organization. Communication pattern of indirect reciprocity is the
most suitable to support this idea. Indirect reciprocity is a communication pattern
characterized by indirect chains of communication that support generalized exchange
[15]. This pattern goes with the principle 1 (Section 4) of agent collaboration. The
meeting room community supports instant messaging.
The agents search for a person who can provide the required data outside the
meeting room. They apply to an online consultant who is not a meeting member. As
131
well as the meeting room community, this consultant accepts the organization culture.
The consultant window supports asynchronous messaging.
Fig. 3 does not provide a specialisation for the activity concept. Examples of
meeting room activities are presenting, discussing, requesting for a service, etc.
Fig. 4 presents the communications between the agents. Firstly, they communicate
with the object to obtain the required data from the resources available in the meeting
room. In the figure, one message to IR agent represents a set of messages sent to
agents responsible for the interactions with the computational and information
resources. Then, when the needed data were not found, they send messages to the
meeting participants. In the messages the agents inquire if the participants are aware
where the data can be found.
One participant recommends requesting a department responsible for data storage.
The agent of this participant sends the message to the online consultant of the
department. In the consultant window it is required to leave the phone number of the
person whom the consultant can call back. The agent leaves the number of the
meeting leader. The data of interest become available after the phone conversation.
The messaging between the agents is displayed to humans as follows. On the
personal devices of the leader meeting and the meeting participants only messages
meant for them are displayed. At that, it does not matter who sent the message (agent
or human). The messages between the IRs' agents are not displayed on the personal
devices of humans.
In the considered above scenario the agent's ontology is used to define the way of
agent communication depending on the agent role and organization culture.
4.2 Smart travelling
Smart travelling demonstrates how “utility” of the solution can be applied to receive a
solution the most "useful" for a particular agent. The following scenario is considered:
One wants to re-fuel the car and have a dinner in a decent restaurant. Instead of
finding a cheapest gas station, the agents find a gas station located near a restaurant,
which has a good feedback from its customers or belongs to the brand preferred by
this person.
IR Meeting room Human's Online Dr.
Agent community Agent consultant Smith
IR's#agent,#
SELECT#SUM(number#of#business#revals)#
GROUP#by#year
No#information Agent,#
SELECT#SUM(number#of#business#revals)#
GROUP#by#year
Request#the#marketing#department
#SELECT#SUM(number#of#business#revals)#
GROUP#by#year
Dr.#Smith
3879034
Call#to#Dr.#Smith
Phone#conversation
Fig. 4. Agent communication in smart meeting scenario
132
The person in the scenario fulfils two roles: driver and restaurant client. For the
presentation below, change of roles is not important. It is supposed that the agent
representing this person fulfils the role of driver.
The scenario solution consists of two actions: visiting a restaurant and refuelling
the car. Besides the user's agent and agents representing resources, three more agents
are involved in negotiation: restaurant advisor, gas station advisor and planner (this
agent, responsible for time keeping, is involved in almost any scenario in order to
avoid solutions, which would suggest driving too far away). Each of the three agents
are assigned certain functions calculating degree of usefulness of their suggestions for
the driver (e.g., visiting a café with average customer ratings has a lowest utility,
visiting a nice restaurant with high customer ratings is estimated has a higher utility,
and visiting the favourite driver’s restaurant has the highest utility). The utility scale
of the planner agent might depend on usual distances driven by the driver, its
preferences and current schedule. The total system's utility of the solution depends on
the contributions of each participating agent. The appropriate mathematical models
are yet to be developed.
In order to such mechanism would operate efficiently, it requires a continuous
adjustment of the agents' utilities. This can be done through collecting information
and knowledge from different resources. The following resources are among them:
1. User feedback (the driver can increase or reduce the utility of a certain service).
This is a reliable information source; however, in real life it is very unlikely, that
the driver will provide such a feedback.
2. Initial driver profile (the driver can fill out the initial preferences in his/her profile).
This is also a reliable information source but such information will be outdated
after some time.
3. Analysis of driver decisions (there can be a resource, which analyses if the driver
followed the proposed solution, or which solution is preferred if several alternative
solutions are presented to the driver). This is a less reliable information source, but
such information will never be outdated and development of learning algorithms
can significantly improve such feedback.
4. Analysis of decisions of drivers with similar interests/habits. This source originates
from the method of collaborative filtering used in group recommendation systems.
The interactions between agents are presented in Fig. 5. They are based on usage of
AppLink (reference) [16] for interaction with the car. The AppLink is in-car
infotainment system that can communicate with third party services and mobile
devices for information driver support. In addition to the information already stored in
the resources (associated databases, user settings, revealed preferences, etc.), the
mentioned above agents acquire information from other resources, namely:
─ Gas station advisor obtains current car location, gas level, and predefined driver
preferences.
─ Restaurant advisor obtains current car location and predefined driver preferences.
─ Planner obtains driver’s schedule from his/her smart phone and predefined driver
preferences to estimate current time restrictions.
133
Car Driver Cloud
AppLink SmartPhone Driver profile Restaurant Gas station Planner
advisor advisor
Gas level
GPS
Preferences
GPS
Preferences
Schedule
Preferences
Navigation Service
Negotiation
Solution transfer to
AppLink Screen & On-Board Navigation
Fig. 5. Agent communication in smart travelling scenario
After that, the agents negotiate in order to generate one or several alternative
solutions based on the driver requirements. During this negotiation, they can query
available navigation system to estimate the driving time between different locations.
Finally, the generated solutions are transferred to the AppLink screen so that the
driver could choose the most appropriate one, and to the in-car navigation system.
5 Conclusion
An agent onlogy empowered by online communities for agent collaboration in CPSSs
and major principles of agent collaboration were proposed. “Utility” of the solution
taking into account preferences of an agent was proposed as the objective function for
agent negotiation. Scenarios of agent collaboration in smart meeting room and in
smart travelling domain were considered. The former scenario illustrates
opportunities the online communities offer for communication of human and non-
human resources of CPSSs. The latter one shows how “utility” of the solution can be
applied to receive a solution the most "useful" for a particular agent role.
The research presented is ongoing. The paper reports some theoretical results. In
the future, an agent negotiation protocol, which would take into account cultural
norms of different online communities and utility of the solution is planned to be
developed.
Acknowledgements. The research was partially supported by projects funded by
grants 15-07-08092, 15-07-08391, 14-07-00427, 14-07-00345, and 13-07-12095 of
the Russian Foundation for Basic Research; project 213 (program 8) of the Presidium
of the Russian Academy of Sciences (RAS); project 2.2 of the basic research program
“Intelligent information technologies, system analysis and automation” of the
Nanotechnology and Information technology Department of the RAS; and grant
074-U01 of Government of Russian Federation.
134
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