Self-interested Nodes Modeling in Mobile Wireless Ad Hoc Networks

Jun Wu, Chongjun Wang and Shifu Chen National Key Laboratory for Novel Software Technology (Nanjing University)

Department of Computer Science and Technology, Nanjing University, Nanjing 210093 wujun@iip.nju.edu.cn

Abstract

The Increasing adoption of the techniques of mobile wireless ad hoc network in the field of civilian applications arouses many urgent problems. As we known nodes to consist a MANET may belong to different users, thus each node aims to pursue the best quality of service for its owner, i.e., they are self-interested nodes trying to achieve the maximum utility. Researchers have shown that selfish nodes in a MANET can greatly decrease the performance of the network, even to the point where no messages can be sent at all. By the similarities between MANET and multi-agent system (MAS), to research some problems in MANET from the view of MAS is an impressive idea. In this paper nodes in MANET are modeled as intelligent agents using an agent programming language named Conceptual Agent Notation (CAN). We don’t use CAN directly but extend its semantics with a set of core operations which capture the essence of ad hoc network, thus provide some solid theoretic underpinning for researching MANET from the view of MAS.

Keywords: ad hoc networks, intelligent agent, multi-agent systems

1. Introduction

The meaning of “ad hoc” is “for this purpose only”,which implies that mobile wireless ad hoc networks(MANET) are systems designed for some special purposes. Actually, the early efforts on MANET are made by the US army to design a new method to communicate in the battlefield. But nowadays civilian devices that utilize this technology are becoming affordable and are very common. People carry numerous portable devices, such as laptops, mobile phones, PDAs and mp3 players, most of which have been equipped with wireless communication interfaces (e.g., blue tooth interface), for use in their professional and private lives. MANET can be deployed amongst them, allowing these devices to communicate data with each other.

The most widespread notion of a MANET is a network formed without any central administration which consists of mobile nodes that use a wireless interface to send packet data. Since the nodes in a network of this kind can serve as routers and hosts, they can forward packets on the behalf of other

nodes and run user applications. But if the nodes to form a MANET belong to multiple users, the case may be somewhat different, that is, each node aims to achieve the best quality of service for its owner, i.e., they are self-interest nodes. Take the scenario that a node receive a request form some other node for forwarding some packets as an example. In this case, the node should reason about the request, i.e., if this process can achieve some profit it may accept the request; otherwise the request will be rejected. Researchers have shown that selfish nodes in a MANET can greatly decrease the performance of the network, even to the point where no messages can be sent at all [3].

MANET nodes are modeled as intelligent agents in this paper, which is based on the semantics of a BDI tradition agent programming language named Conceptual Agent Notation (CAN) [7]. We aim to acquire rational behavior for each node and thus contribute to provide some solid theoretic underpinning for researching MANET from the view of multi-agent system (MAS). BDI (Belief-Desire-Intention) theory is an important theory for modeling intelligent agent which has its roots in philosophy with Bratman’s theory of practical reasoning [6]. BDI agents are systems that are situated in a changing environment, receive continuous perceptual input, and take actions to affect their environment, all based on their internal mental state [4]. They are extremely flexible and responsive to the environment, and as a result, well suited for complex applications with real-time reasoning and control requirements [5].

The rest of this paper is organized as follows. In section 2, the semantics of CAN is briefly discussed. In section 3, a set of core operations which capture the most basic requirements of MANET are introduced to extend the semantics of CAN notation. Moreover, MANET notes are modeled by this extended language. In section 4, some related works are discussed and comparison is given. Finally in section 5, we conclude this paper with future research.

2. CAN notation

Nodes in a MANET are modeled as intelligent agents, named in this paper as ad hoc agents. The semantics of CAN is adopted and extended as the basis of the proposed theory.

Generally speaking, BDI agent-oriented programming languages are built around an explicit representation of beliefs, desires, and intentions. A BDI architecture addresses

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how these components are represented, updated, and processed to determine the agent’s actions which should seem rational in the eyes of the beholders. CAN is a high-level plan language in the style of typical agent languages in the BDI tradition. Its syntax and semantics attempt to extract the essence of a class of implementable agent platforms. We refer to [7] and [5] for a detailed description of CAN. Here we only discuss a most fundamental subset of CAN which is sufficient for a preliminary specification of ad hoc agents.

According to CAN specified in [5], an agent is created by the specification of a set of base beliefs and a set of plans Π . The belief base of an agent is a set of formulas from some (knowledge representation) logical language. The programmer may choose any logical language; what is required is for operations to exist that check whether a condition φ —a logical formula over the agent’s beliefs—follows from a belief set (i.e., φ ), and to add or delete a belief b to or from a belief base (i.e., { }b∪ and \{ }b ,respectively). In practice, however, the belief base contains ground belief atoms in a first-order language. An agent plan library Π consists of a collection of plan rules of the form e : Pϕ ← , where e is an event and ϕ is the context condition which must be true in order for the plan-body P to be applicable. The plan-body or program P is built from primitive actions act that the agent can execute directly, operations to add +b and delete b beliefs, tests for conditions ?φ , and events or (internal) achievement goals !e .Complex plans can be specified using sequencing 1 2;P P ,parallelism 1 2P P , and declarative goals ( , , )S fPφ φGoal .There are also a number of auxiliary plan forms which are used internally when assigning semantics to constructs: basic (terminating) program nil; and compound plans 1 2P P ,which executes 1P and then executes 2P only if 1P failed; and

1 1(| : ,..., : |)n nP Pϕ ϕ , which is used to encode a set of (relevant) guarded plans. The full language is therefore described by the following grammar:

1 2 1 2 1 2:: | | | | ? | ! | ; | | |P nil act b b e P P P P P Pφ= + −

1 1( , , )|(| : ,..., : |)S f n nP P Pφ φ ϕ ϕGoal

Two types of transitions are used to define the semantic of the CAN agent. The first type defines what it means to execute a single intention and is defined in terms of basic configurations. The second is defined in terms of the first type and defines what it means to execute an agent. A basic configuration is a tuple , , P< > consisting of the current belief base of the agent, the sequence of primitive actions executed so far, and the plan-body program P being executed(i.e., the current intention).

The core derivation rules defined on basic configurations for the language is shown as follows:

{ : | : ( , )}, ,! , , (| |)

i i i iP e P mgu e eEvent

eϕ θ θ ϕ θ′ ′Δ = ← ∈ Π ∧ =

< >→< Δ >:

, , (| |) , , (| \ |)i i i

i i

PSel

P Pϕ ϕ θ

θ∈ Δ

< Δ >→< Δ >

1, , bdiP< > ⎯⎯→

1 2 2, , ( ) , ,bdif

P P P< > < >⎯⎯→

?, , ? , , nil

φθφ< >→< >

: ;, , ( \ ) , . ,a a act

actact act nil

ϕ θ ϕθθ θ

− +

− +

← Φ Φ ∈ Λ =< >→< Φ ∪ Φ >

1

1 2 2

, , ) , ,, , ( ; ) , , ( ; )

P PSeq

P P P P

′ ′ ′< >→< >′ ′ ′< >→< >

Based on the above basic rules, the evolution of an agent can be defined. An agent configuration, or just an agent, is a tuple of the form , , , , ,< Λ Π Γ > where is the agent name, Λ is an action description library, Π is a plan library,

is the belief base, is the sequence of actions already performed by the agent, and Γ is the set of current intentions (that is, plan-body programs). Transitions between agent configurations are dictated by the following three rules:

, , , ,, , , , , , , , , , ( \ { }) { }

P P PAstep

P P

′ ′ ′∈ Γ < >→< >′ ′ ′< Λ Π Γ > < Λ Π Γ ∪ >

, , , , , , , , , , {! }e

Aevente

is a new external event′ ′< Λ Π Γ > < Λ Π Γ ∪ >

, ,P P∈ Γ < > →, , , , , , , , , , \ { }

AcleanP′ ′< Λ Π Γ > < Λ Π Γ >

The first rule Astep perform a single step in one intention; the second rule Aevent create a new intention from an external event; and the last rule Aclean removes a completed intention from the intention base, that is, an intention nil or one that is blocked and cannot make a transition.

3. Ad hoc agent

The semantics of ad hoc agent is specified based on the semantics of CAN notation discussed above.

Analyzing the environment is an easy start point to model the behavior of intelligent agent situated in it. In the eye of an ad hoc agent, the environment is simply the overall behavior of others which surround it. By the criterion proposed in [8], the features of the ad hoc environment are listed as follows.

Inaccessible: The perception ability of an ad hoc agent is determined by the maximum transmitting range, which is limited by the battery power of the network nodes. In another word, only part of the entire environment which is within the

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maximum transmitting range can be observed by an ad hoc agent, i.e., an ad hoc agent can only have limited knowledge about the environment.

Un-deterministic: The effect of an action taken by an ad hoc agent is uncertain. For example, given two ad hoc agents A and B, after a request for relaying packets is sent form A to B, the post-condition (i.e., the reply of B) only depends on the reasoning result of B, which is uncertain.

Dynamic: Due to the mobility of ad hoc agents, the environment is extremely dynamic. The set of neighbors an ad hoc agent can communicate with is unceasingly changing, and as a consequence each possible route ever changing also. More precisely speaking, the dynamic attribute of the environment is characteristic by the fact that the environment may change before the point an action is finished (cf., for static environment, it can change only after an action is finished).

Continuous: The environment is continuous other than discrete because the number of states of it is infinite.

The belief base of an ad hoc agent is closely related to the environment. is consisted by two parts of information: the first describes the outside environment (such as adjacent agent, possible route to some destinations, etc.) and the second describes the internal state (such as remain battery, utility, etc.), both of which are specified by a set of first order logic formulae.

The behavior of an ad hoc agent can be encoded into a plan library Π which contains plan rules of the form e : Pϕ ← . Although every event can be handled by the plan library, for the ad hoc agent, we argue that it is necessary to define a set of core operations, which are the most basic operations to support the MANET to function well. Here we assume a set of core operations with five elements as shown in the following definition. The set of core operations is not fixed as elements can be inserted or deleted to or from it according to different applications.

Definition 1. (Core operations) The set of core operations O with typical element o is defined as:

{ , _ , _ , , }O Perception Route Dis Route Ans send forward=

In the above definition, Perception is an operation to observe the network; _Route Dis is an operation to discover a route to a certain destination; _Route Ans is the response after receiving a route discover message from some other ad hoc agents; send is the operation of send some packets and forward is the operation of forwarding some packets for

another ad hoc agent. An operation library is constructed to handle the core

operations.

Definition 2. (Operation library) The operation library Ωhandles the core operations, which contain rules of the form:

1 1(| : ,..., : |)n no P Pϕ ϕ← , where o is a core operation,

1,..., nϕ ϕ are logical formulae used as guards for each plan body 1,..., nP P .

From the definition of operation library we can see that every operation is directly mapped to several guarded plans. By the semantics of CAN, only one plan with its guard true according to the beliefs can be selected and executed. It is self-evident that for an ad hoc agent this process captures the reasoning process for its actions. Then we can formally define ad hoc agents in following definition.

Definition 3. (Ad hoc agent) an ad hoc agent is a tuple , , , , , ,< Λ Π Ω Γ > , where is the agent name, Λ is

an action description library, Π is a plan library, is the belief base, is the sequence of actions already performed by the agent, Ω is the operation library to handle core operations and Γ is the set of current intentions.

The notion of a basic configuration, as defined below, is used to represent the state of an adopted plan (i.e., intention) at each point during computing. It consists of the elements which may change during the execution of the plan.

Definition 4. (Basic configuration) A basic configuration of an ad hoc agent is a tuple , , P< > where is the belief base, is the sequence of actions already performed by the agent, and P is the current executing intention.

The semantics of core operations can be specified by two transition rules, Aoper and CoreOperation. Separately, Aoper is in the plan execution level and CoreOperation is in the agent execution level.

Definition 5. (Core operation handling)

, , , , , , , , , , , , {! }o

Aopero

is a core operation′ ′< Λ Π Ω Γ > < Λ Π Ω Γ ∪ >

1 1

1 1

(| : , ..., : |), ,! , , (| : , ..., : |)

n n

n n

o P POperation

o P PCore

ϕ ϕϕ ϕ

∃ ← ∈ Ω< >→< >

By the CAN notation and the transition rules defined in the above definition we can define the operation semantics of ad hoc agent as: , , , , ?, ,Event CoreOperation Sel f act Seqan , , ,Astep Aevent Aoper Aclean . And by this extended semantics the notion of ad hoc agent is formally specified.

4. Comparison

Many papers about MANET have been presented from the view of multi-agent system aiming to solve the problems brought about by self-interest nodes [3, 9-12]. The difference is that most of them are based on game theory which is a

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macroscopical aspect of MAS. Using ideas from game theory, simple punishment mechanisms have been designed to solve this problem [9] [11]. Altman et al [9] and Srinivasan et al [11] attempt to solve this non cooperative game problem by looking at previous work done in game theory. Both come to the conclusion that tit-for-tat is a good algorithm that can be used to provide incentives for nodes to cooperate. Reputation mechanisms define a method for keeping track of a node’s history by monitoring the node’s past actions, and then use this history in order to decide whether or not to forward packets for that node [10] [11]. This is basically a more complicated punishment system in which each node is tracked separately and punished only based on its own previous actions. Another approach to solve this problem is the implementation of pricing mechanisms. In these mechanisms, money is charged to nodes that cause traffic and money is given to nodes that help forward packets. One well referenced mechanism that does this is SPRITE [12]. This mechanism not only uses game theory to describe node’s behaviors, but it also models reporting payments as another multi-agent game and uses game theory to help decide how to set the payments.

We research this problem from a microcosmic aspect by specifying the semantics of each ad hoc agent, which can be seen as provide a theoretic basis for above researches. The extended semantics proposed in this paper can be used to create agent architecture which meets the requirement of MANET environment. The aim of our research is to achieve rational behavior of each ad hoc node using the theory of intelligent agent.

5. Conclusion and future work

Nodes in the MANET are modeled as ad hoc agents in this paper. We mainly aim to provide a mechanism by which the nodes in the ad hoc networks can produce rational behaviors which is necessary in a MANET consisted by self-interested nodes. A set of core operations with its semantics specified by transition systems is added into the CAN notation, and with this extended semantics the notion of ad hoc agent is formally specified. We argue that this work has provided some solid theoretic underpinning for researches on MANET from the view of multi-agent system.

We only model the control follow of a single ad hoc agent in this paper, the specification of action description library, plan library and operation library is beyond the scope of this paper and we leave them to our future researches. What’s more, the interaction of ad hoc agent’s is an important issue which is the ultimate aim for a MANET. In our future research, we will also model the interaction of ad hoc agent based on the semantics proposed in this paper.

6. Acknowledgement

This paper is funded by NSFC of China (NO. 60503021), Natural Science Foundation of Jiangsu Province (NO.BK2005075) and High-tech Research Program of Jiangsu Province (NO. BG2006027).

7. References

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[3] S. Marti, T. J. Giuli, K. Lai, and M. Baker, “Mitigating routing misbehavior in mobile ad hoc networks”, Mobile Computing and Networking, 2000, pp. 255–265.

[4] A. S. Rao, “AgentSpeak(L):BDI agents speak out in a logical computable language”, In MAAMAW’ 96, pages 42–55. Springer Verlag. LNAI, Volume 1038, 1996.

[5] S. Sardina, L. de Silva, L. Padgham, “Hierarchical planning in BDI agent programming: a formal approach”, In proceeding of AAMAS’06, 2006.

[6] Bratman, M., Intentions, Plans, and Practical Reason, Harvard University Press, 1987.

[7] M. Winikoff, L. Padgham, J. Harland, and J. Thangarajah, “Declarative & Procedural Goals in Intelligent Agent Systems”, In Proc. of KR-02, 2002, pp. 470–481.

[8] Wooldridge, M., An Introduction to MultiAgent Systems, John Wiley&Sons, LTD, 2001, pp. 76-79.

[9] E. Altman, A. Kherani, P. Michiardi, and R. Molva, “Non-cooperative forwarding in ad-hoc networks”, Technical Report INRIA Report No. RR-5116, 2004.

[10] R. Molva P. Michiardi, “Core: A collaborative reputation mechanism to enforce node cooperation in mobile ad hoc networks”, Institut Eurecom Research Report, 2001.

[11] V. Srinivasan, P. Nuggehalli, C. Chiasserini, and R. Rao, “Cooperation in wireless ad hoc networks”, In Proceedings of IEEE Infocom, 2003.

[12] S. Zhong, Y. Yang, and J. Chen, “Sprite: A simple, cheat-proof, credit-based system for mobile ad hoc networks”, Technical Report Yale/DCS/TR1235, Department of Computer Science, Yale University, 2002.

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