AISIMAM An Artificial immune system based intelligent multi agent model and its application to a mine detection problem

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1 Rochester Insttute of Technology RIT Scholar Works Presentatons and other scholarshp 22 AISIMAM An Artfcal mmune system based ntellgent mult agent model and ts applcaton to a mne detecton problem Srvdhya Sathyanath Ferat Sahn Follow ths and addtonal works at: Recommended Ctaton Sathyanath, Srvdhya and Sahn, Ferat, "AISIMAM An Artfcal mmune system based ntellgent mult agent model and ts applcaton to a mne detecton problem" (22). Accessed from Ths Conference Proceedng s brought to you for free and open access by RIT Scholar Works. It has been accepted for ncluson n Presentatons and other scholarshp by an authorzed admnstrator of RIT Scholar Works. For more nformaton, please contact rtscholarworks@rt.edu.

2 AISIMAM An Artfcal Immune System Based Intellgent Mult Agent Model and ts Applcaton to a Mne Detecton Problem Srvdhya Sathyanath Dept of Electrcal Engneerng, Rochester Insttute of Technology, sxs4446@rt.edu Abstract Artfcal Immune System (AIS) s a novel evolutonary paradgm nspred by the bologcal aspects of the mmune system. The human mmune system has motvated scentsts and engneers for fndng powerful nformaton processng algorthms that has solved complex engneerng tasks. Ths paper dscusses two concepts. (a) The behavoral management of artfcal ntellgence (AI) namely the ntellgent mult agent systems, (b) The evolutonary computaton called the artfcal mmune system that mtates the bologcal theory called the mmune system. The outcome of ths research s an Artfcal Immune System based Intellgent Mult Agent Model named AISIMAM that solves agent-based applcatons. The model s appled to a mne detecton and dffuson problem and the results prove that AISIMAM has solved the problem successfully. 1 Introducton The study of bologcal systems s of nterest to scentsts and engneers as they turn out to be a source of rch theores. They are useful n constructng novel computer algorthms to solve complex engneerng problems. Genetc algorthms derved from the prncples of genetcs, Neural Networks derved from bran - nervous systems or neurology (Dasgupta & Attoh-Okne, 1997) and cellular engneerng based on cell bology are some of the bologcally motvated evolutonary algorthms that perform nformaton processng tasks. Immunology as a study of the mmune system (Elgert, 1996) nspred the evoluton of artfcal mmune system, whch s an area of vast research over the last few years. Artfcal mmune system mtates the natural mmune system that has sophstcated methodologes and capabltes to buld computatonal algorthms that solves engneerng problems effcently. The man goal of the human mmune system s to protect the nternal components of the human body by fghtng aganst the foregn elements such as the fung, vrus and bactera (Tmms et al., 1999). It s nterestng to observe that the process of recognton, dentfcaton and post processng nvolve several Ferat Sahn Dept of Electrcal Engneerng, RIT 79 Lomb Memoral Drve, Rochester, NY feseee@rt.edu mechansms such as the pattern recognton, learnng, communcaton, adaptaton, self-organzaton, memory and dstrbuted control by whch the body attans mmunty (Dasgupta, 1999). AIS has made sgnfcant contrbutons to machne ntellgence. Applcatons of AIS are not lmted to optmzaton, robotcs, neural network approaches, data mnng and mage classfcaton (Hajela & Yoo 1999; Ishguro et al., 1997; Hoffmann 1986; Hunt & Fellows 1996; Sathyanath & Sahn, 21). In ths paper, we concentrate on Mult Agent Systems (MAS) and ther characterstcs. Mult agents are populaton of agents, (.e.), more than one agent reacts to the change n envronment to accomplsh the task (Huhns & Sngh, 1998). Mult agent systems are based on behavor management of several ndependent agents (M. Wooldrdge, 1999). The objectve of the authors was to develop a bologcal based ntellgent mult agent archtecture. Mult agent systems have some features n common wth the mmune system and provde scope for applyng mmune system methodologes. Therefore, we have appled artfcal mmune system to mult agent systems for the computatonal ntellgence of agents. The outcome of the research s a generc Artfcal Immune System based Intellgent Mult Agent Model named AISIMAM. The model draws an analogy between the mmune system and agent methodologes. It apples the mmune system prncples to the agents to perform a global goal n a dstrbuted manner. AISIMAM s appled to mne detecton and dffuson problem, a specfc applcaton expermented to prove the model. Ths paper shows that AISIMAM solves the mne detecton applcaton successfully. The organzaton of ths paper s as follows. Secton 2 presents a bref ntroducton to the mmune system. Secton 3 dscusses agent defntons, characterstcs of mult agents n problem solvng. Secton 4 focuses on AISIMAM wth the mathematcal dervatons and explantons. Secton 5 explans the need for the mathematcal representaton and Secton 6 demonstrates the applcaton of AISIMAM to a mne detecton and dffuson problem. In Secton 7 we state the new aspect of ths research and n Secton 8 we state the scope for

3 future work. Secton 9 summarzes the concluson derved out of ths research work. 2 The Human Immune System The natural mmune system s a very complex system wth several mechansms for defense aganst nfectous agents enterng our system. The external components to the mmune system are antgens or called the non-self cells, as they are foregn substances to the body. The basc components of the mmune system are the whte blood cells, called self-cells or lymphocytes n mmunologcal terms. These specalzed cells are classfed nto two types namely the B lymphocytes and T lymphocytes. B-lymphocytes are the cells produced by the bone marrows T cells develop n bone marrow and mature n thymus The major responsblty of the B cells s the secreton of the receptors called the antbodes (Ab) as a response to the antgens that enter the body (Ag) (Hajela & Yoo, 1999). The role of these receptors on the surface of the B cell s to recognze and bnd the antgen. These receptors are called dotopes and paratopes. Antgens also have receptors called eptopes. The B cells generate antbodes of complementary match that recognzes and bnds the antgen (Castro & Von Zuben, 1999). Complementary match means the generaton of an opposte shape or structure that fts well wth the antgenc eptope to recognze the antgen. The receptors of the B cell change ther shape accordng to the shape of the eptope (Tmms et al., 1999). Fgure 1 shows the B cell, B cell receptors and the eptopes of the antgen. B cell Antgen Eptope of the antgen B cell receptor Fgure 1: B cells, B cell receptors, antgen, and eptopes. 2.1 Propertes of the Human Immune System Ths secton brefly dscusses some of the propertes of the mmune system by whch the human body attans mmunty. The man functon of the mmune system s to kll the antgen. It s nterestng to note that ths common goal of the system s handled by the ndvdual components of the mmune system n a dstrbuted fashon. At the same tme they also have remarkable propertes wth whch they work collectvely to perform the task. The mmune system possesses the followng propertes. Postve and negatve selecton s a process of dscrmnaton of self/non-self cells that prevents autommuno dseases. Ths process flters out the cells that would work aganst the self-cells and only the cells that would not bnd the self-cells crculate to fght aganst the antgens (Dasgupta, 1999). Clonal selecton and expanson s a process of selecton of useful cells that recognze the antgen and reproduce those cells. Ths process of clonng multples the useful cells that are capable of recognzng the antgens. Therefore, the B cells that contan the specfc receptor that match a partcular antgen are also multpled. In ths process, the clones suffer hypermutaton that alters the shape of the receptor also called receptor edtng, thus ncreasng the affnty between the clone and the specfc antgen (Burnet, 1978; Dasgupta, 1999). Immune memory s a result of clonal expanson. Some of the cloned cells dfferentate nto memory cells and the rest of the clones become plasma cells. B cells remember the shape of the antgen that they have fought and recollect when they see the same antgen agan. Ths process defned as secondary response, s a feedback of the past event for a current nput. Ths process helps the system to learn and s called as renforcement learnng. Plasma cells produce cells wth hgher affntes (Castro & Von Zuben, 1999). Jerne s dotropc network deals wth the nteracton of antbodes. Jerne s network s a network of B cells that communcate the shape of the antgenc eptope amongst them through dotopes and paratopes. Ths also transforms the receptors accordng to the antgenc pattern. Ths shape transformaton s an mportant role of nformaton transfer and communcaton between the B cells (Jerne, 1984). Fgure. 2 show the overall functonng of the mmune system. The mmune system recognzes the antgens and the antgenc patterns are dentfed. On dentfcaton of an antgenc pattern, the B cells communcate the nformaton n parallel to each other by means of paratopes and dotopes n the network. Paratopes match wth the eptopes of the antgen to recognze the antgen. Paratopes also change ther shape to strengthen the bond between the eptope and the paratope. However, the bndng stays only for a short tme called the tolerzaton perod (Hofmeyer, 2) wthn whch a number of receptors should bnd the antgen. When ths process of bndng wthn a short perod happens, the B cells gets actvated and performs a set of actons to kll the antgen (Hofmeyer, 2). On actvaton, every B cell responds by changng the shape of the receptor accordng to the antgenc eptope. B cells that have hgher affnty towards the antgen are the ones that recognze the antgen. The useful cells undergo multplcaton by clonal

4 expanson and produce hgh affnty cells or clones. Snce the antgen has multple eptopes and the B cells are monospecfc (Castro & Von Zuben, 1999) wth a sngle type of receptor, B cells work together to kll the antgen through mmune network. Part of the clones dfferentate nto plasma cells that create hgher affnty cells and the rest turn out to be memory cells that remember the antgen that was destroyed. Thus the human system attans mmunty aganst the antgens. Pattern recognton B cells Memory Ag stmulus Clonal expanson and selecton B c B cells B c B c B cells Immune network Plasma cells Fgure 2: Representaton of the human mmune system. 3 Mult Agent Systems Mult agent systems (MAS) deal wth the behavor management n collecton of several ndependent enttes, or agents (Wooldrdge, 1999). There are several defntons for agents. We have chosen two defntons of agents. Nwana and Ndumu defned an agent as a component of software and/or hardware whch s capable of actng n order to accomplsh tasks on behalf of ts user (Nwana & Ndumu, 1997). Agents that operate robustly n rapdly changng, unpredctable, or open envronments, and where there s a sgnfcant possblty that actons can fal are known as ntellgent agents or sometmes called autonomous agents (Bond & Gasser, 1998). Agents can exst alone or n a socety of agents called mult agents (MAS). Mult agents are a populaton of agents, that s, more than one agent can change the envronment to accomplsh the task. They are dstrbuted computatonal systems (Cho & Tae-Lm, 21) n whch each agent n MAS has a lst of ndvdual goals or tasks that t wll perform. At the same tme, MAS has global goals that all the agents wll strve to acheve where the ndvdual efforts of each member agent are put together toward reachng the MAS s global goals (Huhns & Sngh, 1998). The advantage of the MAS s that the lmtatons of the ndvdual capabltes of the agents are elmnated (Abul et al., 2). Agents wth a fxed goal learn how to change the envronment to acheve the end goal. Ths process s called renforcement learnng n agents. In order to acheve an ndependent and global problem solvng, the agents behave accordng to ts defned characterstcs. Some of the characterstcs of agents that defne ther behavor are autonomy, frendlness, reasonng, learnng, communcaton and coordnaton mechansms. Smlarly, there are dfferent envronments accordng to whch the agents perform the goals. The mult agent envronment s usually open, decentralzed, and contans autonomous agents (Huhns & Stephens, 1999). In summary, agents are enttes wth well-set goals, actons and knowledge n an envronment that senses, communcates, coordnates, learns and makes decsons accordng to the envronment (Cho & Tae-Lm, 21). The followng secton brefly descrbes some of the characterstcs of the agents and dfferent knds of envronment (Mohammed, 2). 3.1 Characterstcs of the Agents and the Envronment The characterstcs of the agents are as follows. 1. Autonomy n agents s a measure of self-suffcency. The agents that operate on ther own are ndependent agents, and f they are restrcted by external nfluences then they are called controlled agents. 2. Socablty s a behavoral measure of an agent to thnk about tself or about others. An altrustc agent acts regardful of others benefts, and s unselfsh. In contrast, an egostc agent acts wth excessve thoughts of self and s self-lovng. 3. Agents could be frendly and be cooperatve or compete wth each other. 4. Agents are classfed nto reactve and delberatve accordng to ther level of cognton. The former ones sense and react n a tmely manner for an envronmental change and the latter ones reason out before makng actons. 5. Moblty determnes f the agents are statonary or tnerant. Statonary agents do not move and tnerant agents are moble. Other characterstcs of the agents that deal wth the agent s adaptablty, ratonalty and localty can be referred to the lterature (Mohammed, 2). An agent may have a problem n decdng whch of ts actons t should perform n order to best satsfy ts desgn objectves. The complexty of the decson makng process can be affected by a number of dfferent envronmental propertes. The followng are varous envronments stated by Russell and Norvg. (Russell &

5 Norvg, 1995; Mohammed, 2). An accessble envronment s one n whch the agent can obtan complete, accurate, up to date nformaton about the envronment s state. The more accessble an envronment s, the smpler t s to buld agents to operate on t. Complex envronments lke the physcal world are defned as naccessble envronments. There are also other knds of envronments. Determnstc envronment and non-determnstc envronment deals wth the certanty of agent s acton. Epsodc and nonepsodc envronment deals wth the performance of agent s n dscrete epsodes wthout any lnks or lnked actons wth the past and current data respectvely. 4 AISIMAM - Artfcal Immune System Based Intellgent Mult Agent Model The backbone of AISIMAM nvolves mtatng the human mmune system n terms of features and functons n mult agent systems. The motvaton for ths research comes from the fact that artfcal mmune system has found solutons for several applcatons. In the same context agent based solutons have also been developed n dfferent applcaton domans (Cho & Tae-Lm 21, Abul et al., 2). The reason for developng the AISIMAM s due to the smlartes observed between the mmune system archtecture and the archtecture of the agents. The dstnct smlartes between the agents and the mmune system are Both are dstrbuted or decentralzed systems Both have multple autonomous enttes Both have ndvdual and global goals Both systems learn from ther experence Both are adaptable Both sense the changes n the envronment and act accordngly Both systems communcate and coordnate Both possess knowledge wth whch they make ntellgent decsons. Therefore, mmune system based mult agent archtecture s dervable. The followng secton descrbes the mult agent systems wth necessary comparsons and explanatons. 4.1 Comparson of AIS and Mult Agent System Parameters The model defnes the non-self cells (antgens) and self-cells (B & T cells) as two agents wth dfferent characterstcs and goals. Therefore, the two types of agents n AISIMAM are Antgens are modeled as non-self agents (NAGs) and Lymphocytes or self-cells corresponds to selfagents (SAGs) We defne the envronment to be a matrx n whch both the NAGs and the SAGs operate. The envronment can be any one of the types of envronment explaned n secton 3.1 dependng on the applcaton. We assume that there s an nformaton vector for each non-self agent. Ths could represent a dsturbance n a process, malfuncton or a vrus n a computer network dependng on the applcaton. The nformaton vectors correspond to the eptopes of the antgen. Smlarly, each self-agent has an nformaton vector that defnes the self-goals. The nformaton vectors correspond to the receptors of the lymphocytes. The nformaton vector can contan a sngle datum or multple data. For example, the nformaton could be a locaton nformaton, dentfcaton number, text nformaton, or all of them dependng upon the applcaton. We consder ths nformaton to be the dotopes and the paratopes. However, the model does not dstngush between the paratopes and dotopes. Instead, the target wll be to perform the end goal wth the avalable nformaton by each self-agent. The end goal could be destroyng the non-self agent as the antgen s klled n the IS, or t can be to dentfy the best acton sets of each self-agent to react to the non-self agent s acton vector. Ths s however problem dependent. The nformaton vectors and the characterstcs of the self and the non-self agents dffer from each other. Ths s smlar to the structures of the eptopes of the antgen and the paratopes of the lymphocytes. In other words, the agents perform ndvdual actons or goals determned by the acton generator functon and the global goal s the coordnated actons of the ndvdual SAGs. The ndvdual acton of the agent corresponds to the receptor shape change n a B cell and the coordnated actons correspond to a group of B cells kllng the antgen. The SAGs are assumed to have sensory capablty to dentfy the NAG wthn a regon called sensory neghborhood. They also possess the capablty to communcate the NAG nformaton to the other SAGs wthn a regon called communcaton neghborhood. The model assumes that the communcaton neghborhood s greater than the sensory neghborhood. Ths s n comparson wth the capablty of the B cells to recognze the antgenc pattern wthn a partcular neghborhood. In mmune system, the communcaton crcle s analogous to communcaton between B cells connected n the mmune network (Jerne s Network). In other words, every B cell communcates the nformaton to another B cell that s wthn the communcaton neghborhood n the mmune network. The agent model descrbes fve stages of processng namely Pattern recognton, Bndng process, Actvaton process, Post actvaton process and Post processng. In pattern recognton, SAGs recognze the presence of the antgen by the stmulaton functon and dentfes the NAGs by an dentfer functon. The model defnes an affnty functon that calculates an affnty value between the actons of the self and the non-self agents. Ths process s defned as bndng process. In the mmune system, the affnty s proportonal to the bndng between the B cell receptors and the eptopes. The affnty

6 calculaton n the agents s smlar to the affnty between the eptope of the antgen and the receptor of the antbody. However, the bndng s not modeled separately n AISIMAM. For nstance, the affnty functon could be a dstance metrc such as the Eucldean dstance. In order to mtate the IS, n the actvaton process we choose the affnty values that are greater then a set actvaton threshold. Actvaton threshold wll help the agents to fnd out the hgher affnty actons called mature actons that are closer to the desred goal. Here, we defne the bndng perod as the tme taken by a number of agents to bnd the NAG. The model defnes ths tme as a sum of recognton tme and groupng tme. Recognton tme s the tme taken by every agent to recognze the NAG and s the same for every agent. The groupng tme s the tme taken by the other agents to react to the dentfed NAG and ths tme dffers from agent to agent. The post actvaton process nvolves clonng. Here, the agents are reproduced wth the mature acton. A part of these cloned agents dfferentate nto memory agents contanng the matured acton obtaned as a result of a partcular NAG. The rest of the clones become plasma agents that create hgher affnty actons through the acton generator functon. Post processng nvolves the prmary and secondary response of mmune memory, whch s also ncluded n the model. Hypermutaton n agents s the process of generatng new actons exsts conceptually. Once the end goal s reached, memory agents remember the actons performed to reach the goal. All the self-agents work n an agent network smlar to Jerne s network. The process of nformaton transfer and communcaton between the agents s an analogy of the agent network to the mmune network. The nature of the agent network s applcaton dependent. Suppresson n the agent network s determned by the suppresson functon. In mmune system, even n the absence of the antgenc stmulus, the B cells perform suppresson. In AISIMAM, n the absence of antgenc stmulus suppresson s performed. The overall representaton of the AISIMAM s shown n Fgure AISIMAM - Operatonal Scheme and the Mathematcal Representaton Ths secton deals wth the notatons used n the model, followed by the defntons of the parameters, and the algorthm Parameter Defntons In the model, we defne the agents namely the self agents (SAGs) and represent them by S, where = 1, 2 N and the non-self agents (NAGs) as N j where j = 1,2...M. We defne the problem doman or the envronment E by E = S N j, j. For all S E, there exsts an nformaton vector of n elements gven by B = [ b1, b2 bn ]. For all N j E, there exsts an nformaton vector of m elements gven by j A = [ a1, a2... am ]. Defne T a to be the actvaton threshold. Receptors B cells SAG SAG Jerne s Immune Network SAG SAG comm. NAG Sensory Neghborhood Antgenc Stmulus Eptopes Communcaton Neghborhood Eptopes SAG SAG Memory Envronment Fgure 3: Representaton of AISIMAM An AIS based Intellgent Mult Agent Model AISIMAM - Algorthm Intalze all the parameters defned as above For each S Calculate 1 ( j M j, = f A, B ) where B s the nformaton vector of S, and A j s the nformaton vector N j n the sensory neghborhood N s j j f noa f 1 ( A, B ) = n N j s f an A If ( M j, ) o o The nformaton about the NAG s transmtted to the other SAGs through the mmune network For each NAG N j, wthn the N s, the sensory crcle where j = 1, 2 e, and e M 1. Pattern Recognton and Identfcaton Identfy the NAG usng the dentfer functon I that s gven by I j = f 2( A j ) Generate possble new actons U j... U k usng acton generator functon that s a functon of I j j 2. Bndng Process U = f ( I ) where j = 1... k 3 j Fnd the affnty for all possble vectors affnty functon U j by the

7 3. Actvaton Process Af j = f4 ( U j ), j = 1...k Choose mature actons whose affnty s greater than actvaton threshold T a and store n the acton set Y Y = { U j Af j > Ta } where j = 1, 2...p The actvaton of the mature actons wthn the bndng perod t b s gven by U j = f5 ( Y, t b )* [ u ( t ) u ( t t b )] End For where u(t) s the unt sep response f no actvaton f 5 ( Y, tb ) = f actvaton If a best acton needs to be chosen, the threshold should be chosen so hgh that p = Post actvaton processng - Clonng If ( U j ) In ths case, agents are reproduced wth mature acton set Y n SAGs. S s cloned wth mature acton set Y to generate q SAGs. c S End If where c = N + 1,..., N + q 5. Post processng - Memory Choose s number of memory agents cloned agents If ( U j ) a c M z = S where z = N + 1, L, N + s, where s < q Memory Response a M z from the The effcency of the prmary and secondary responses are gven by η p = f 6 [N p, T p ] η s = f 7 [N s, T s ] where T p >>T s and N p << N s and N p and N s are the number of actons requred to kll the NAG n the prmary response. T p & T s are the tme taken for the prmary and secondary responses respectvely. The effcency of the prmary and secondary responses s η p and η s respectvely. Plasma Response z Rest of the clones are defned as plasma agents S where z = N+ s + 1,..., N + q. Here q-s agents are added nto the system. End If End For Else perform suppresson by the suppresson functon j P, j = f8( B, B ) where, j are of S and S j End If 5 Need for a Mathematcal Representaton The goal of AISIMAM s to provde a mathematcal representaton for the operaton of mmune system. Several mmune modelng such as the mmune network model (Castro & Von Zuben, 21), negatve selecton algorthm (Dasgupta), mathematcal modelng of the clonal selecton (Chowdary, 1999) and mmune memory (Smth et al., 1996) agent based mmune systems (Mor, Tsukyama and Fukuda 1997, Dasgupta 1998) exst n the lterature. AISIMAM dffers from the other models n the context of mathematcal functons defned for the entre process. In order to prove the usefulness of the representaton, two applcatons namely bar code recognton and mne detecton are compared. In the case of barcode recognton, assume that the nonself agents N j or antgens are the characters to be recognzed. The B cells are the software agents S whose nformaton vector contans the correspondng ASCII characters. Each agent has a defned group of characters. Envronment E has the nformaton about the recognzed and the unrecognzed characters. If the agent can recognze the character, recognton s acheved. Otherwse the agents can communcate through the envronment to fnd f the unrecognzed character falls nto ts category. The stmulus M s defned by the recognton of the start bt pattern of the barcode that defnes the start of the recognton process. The dentfer functon I s a character recognton functon. The affnty functon Af can be defned as the matchng functon between the recognzed character and the character n the agent s nformaton vector. Affnty threshold T a can be set to 1 that chooses the best match. In ths case clonng s not utlzed. Thus the agents are not reproduced. In ths applcaton, sensory and communcaton neghborhood s zero, snce the agents are not n a space. In the case of mne detecton applcaton, non-self agents are the mnes and the moble robots are the self-agents. In ths case, both are hardware agents. The sensory and communcaton neghborhoods are defned by the dstance metrc. The dentfer functon I becomes fndng the mne by the dentfer and the locaton of the mne. The affnty functon Af s the Eucldean dstance. Affnty threshold T a can be set to a predefned value. Mne detecton applcaton s explaned n detal n the followng paragraphs. As can be seen above, the model can be appled to dfferent applcatons by changng the functons. Therefore, the generalzed functons provde a global representaton for several agent based applcatons. 6 Applcaton of AISIMAM to a Mne Detecton Problem To expermentally verfy the archtecture, AISIMAM s appled to a specfc problem. The problem mplemented s mne detecton and dffuson. The experment s

8 smulated n MATLAB. The followng secton dscusses the parameters of AISIMAM used for ths specfc applcaton and the pseudo code for the problem. 6.1 Parameter Defntons The followng secton brefly descrbes the characterstcs of NAGs, SAGs and envronment for mne detecton NAGs and ts characterstcs The antgen or the Nonself agent (NAG) s the mne. Defne the area to be explored for detectng the mne. Ths defnes the boundary of the envronment for the agents to detect the mne. Mnes are deployed n a unform dstrbuton wthn the envronment. The ntal locatons correspond to the eptope or the receptor of the antgen. Characterstcs of the mnes are statonary, unfrendly and compettve. Crclng the mne s defned as dffusng the mne SAGs and ts characterstcs Defne the B cells to be the self-agents (SAGs). Deploy all the SAGs n a unform dstrbuton wthn the envronment. The ntal locatons of the SAGs correspond to the receptors of the B cells. Characterstcs of the SAGs are tnerant, ndependent, cooperatve, altrustc and delberatve. In mne detecton applcaton, t s assumed that the envronment s accessble and the self-agents get updated nformaton about the envronment. Assume that all the SAGs have the capablty to sense the mne and communcate between the agents wthn the sensory and communcaton crcles respectvely. We have used Eucldean dstance measure for both the cases. Every SAG (robot) recognzes the mne and dentfes the locaton of the mne wthn ths sensory crcle. On dentfcaton of the NAG (mne) every SAG communcates to the other SAGs n a Jerne s network. For ths problem, we have assumed Jerne s network as a broadcast network. It s also assumed that the communcaton between the SAGs s larger than the capacty of every SAG to sense the NAG Pseudo Code For The Mne Detecton Problem The pseudo code for the mne detecton problem s as follows. 1. Intalze the SAGs and NAGs n a unform dstrbuton. 2. dff_use = ; (Intally there s no dffuson) 2.1 Whle (dff_use number of_mnes, N ) 2.2 For each SAG S j, do the followng If (there s a mne wthn the sensory crcle) a) Identfy the locaton of the mne b) Inform the locatons of the mnes to the other self-agents wthn the communcaton crcle. Ths corresponds to the communcaton through the mmune network. c) SAG generates new actons that are eght dfferent new locatons to move d) Fnd out the dstance (affnty functon) between these locatons and mne locatons. The Affnty s calculated by the Eucldean dstance between the generated locatons and the robot locaton. e) Choose the dstance that s lesser than an affnty threshold and move to that locaton. f) If (ths locaton s the mne locaton) If (there are 4 SAGs around the mne) Dffuse the mnes, update the number of mnes dffused, (dff_use = dff_use + 1); If (dff_use == number of mnes), Break; End If; End Whle STOP Else wat untl there are four SAGs around the mne; End If Else do step 2.2. c. End If Else If (there are any self-agents wthn the Communcaton crcle) If (non-self nformaton s avalable) repeat from step 2.2. End If Else Make random movements from the current locaton, snce there s no NAG nformaton from other self-agents and no mne detected wthn the sensory crcle End If; End For; End Whle; STOP Memory s not used n ths problem snce there s no usefulness n rememberng the locaton of the mne once t s detected and dffused Smulaton Results We assume that a pror knowledge of the mnefeld ntensty s known n the gven envronment. In the smulaton, ths means that the number of mnes n the gven envronment s known. Therefore known number of mnes s deployed n a unformly dstrbuted manner n the gven area. Ths creates the mnefeld. We also deploy a known number of moble robots n a unformly dstrbuted manner n the envronment. The smulaton dfferentates the moble robot and the mne by usng a + for a mne and a o for robots for representaton whle the code dentfes a mne by a and the robot by a 1. The nformaton vector for the mne and the robots contan the ntally deployed locaton nformaton along wth the dentfer. Table 1 shows an example of the mne and the robot nformaton vector. The smulaton also requres settng the sensory crcle of the robot and the

9 communcaton crcle. We have assumed that the communcaton crcle s greater than the sensory crcle. to solve the mne detecton problem successfully. Table 1: An Example of Informaton Vector of Mnes and Robots X coordnate Y coordnate Identfer Mne Robot The smulaton s verfed for the followng varatons. By ncreasng the sensory range from 3 to 9 unts of dstance measure. The communcaton crcle was vared between 5 and 11 unts of dstance measure. Changng the envronments area to 1 x1 and 32 x 32 rectangular grds. Here, the envronment s accessble where each SAG has the nformaton about the mnes and the other SAGs n the sensory and communcaton neghborhood. That s, on dentfcaton of the mne, SAGs wthn the communcaton crcle exchange about the number of mnes detected and ther respectve locatons through the agent broadcast network. A sample envronment vector s shown n Table 2. It can be seen from Table 2 that the robot 1 has the nformaton about mne 1 that s accessble to robot 2 f t s wthn the communcaton crcle because robot 2 checks for the nformaton avalable wth robot 1 snce t has not dentfed any mnes. However, the envronment becomes naccessble on the assumpton that the envronment s not updated or when the communcaton crcle s zero (c_cr = ). It s useful to make the envronment accessble n practce because, the moble robots for mne detecton can be provded wth the capablty to communcate. Table 2: An Example of the Envronment Vector Index Mnes 1 2 Robots 1 2 Coordnat es (Intal) X Y Identfer 1 1 No of mnes detected 1 Detected Mne locatons ,5 -- The experment s repeated for dfferent populatons of mnes and robots. The typcal range for the mnes deployed are vared between 1 and 7 and accordngly and the robots are vared between 4 and 1. Fgures 4 and 5 show the smulaton wth mnes and robots wth ther ntal locatons and the four agents surroundng the mne. The followng results prove that AISIMAM s able Fgure 4: The locatons of mnes and robots after 2 teratons Fgure 5: Four robots have crcled one mne after three teratons Observatons The followng cases are studed and results are shown below. a) For an ncrease n the populaton of mnes and ncrease n populaton of robots the computatonal complexty n terms of rate of convergence (or the number of steps needed for the algorthm to converge) s studed. For an envronment sze of 32x32 and a constant sensory and communcaton crcles, the ndvdual rates of convergence are shown n Fgures 6 and 8 and the average convergence rate can be seen n Fgures 7 and 9. In Fgures 6 to 9, x-axs s the number of mnes, y- axs s the number of agents and z-axs s the number of teratons. b) For an ncrease n the sensory regon and commu ncaton regon the computatonal tme n terms of rate of convergence s studed. Increasng the sensory and communcaton crcles reduce the requred the number of steps for the algorthm to converge. Ths s due to the fact that robots senses more area and can communcate wth more robots and check f others have mne nformaton f they cannot fnd any. The experment s repeated for the same number of mnes and number of robots wth a step ncrease n the sensory and communcaton crcles n the followng combnatonal pars (3,5), (5,7), (7,9) and (9,11). The number of teratons for a chosen value of robots and mnes can be seen n Fgures 6 and 8. The Fgures 7and 9 shows the average number of teratons for sensory and

10 communcaton crcles to be (5,7) and (7,9). However t was observed that ncreasng the sensory and communcaton crcle reduces the average number of teratons for the algorthm to converge. because the parameters of the model can be changed by the formulated functons dependng on the applcaton. Fgure 6: The rate of convergence for varaton n mnes and agents for 32x32, sen_c = 5, c_cr = 7 Fgure 7: The average rate of convergence for varaton n mnes and agents for 32 x 32, sen_c =5, c_cr = 7 Fgure 8: The rate of convergence for varaton n mnes and agents for 32 x 32, sen_c = 7, c_cr = 9 7 New Aspect of the Work Lterature survey shows that there are several applcatons on Artfcal Immune Systems and Mult Agent Systems ndependently. Some of the recent work also addresses some of the propertes of AIS to agent systems to solve a partcular task (K. Mor, M. Tsukyama and M. Fukuda 1997, D. Dasgupta 1998). AISIMAM s a generc model that provdes to defne the SAGS and NAGS n terms of functons to be determned by the applcatons. Indvdual goals and a global goal for the agents can also be defned by the functons. The model s flexble and unque Fgure 9: The average rate of convergence for varaton n mnes and agents for 32x32, sen_c =7, c_cr = 9 8 Future Work A mathematcal representaton of the mmune network s expected to be added n the future. Further conclusons can be arrved from the followng addtons. In the mne detecton applcaton, a) We have assumed that the robots themselves do not get destroyed n the detecton and dffuson process. But n practce, a robot can fall on the mne durng deployment. So n future, the algorthm can be modfed to analyze the case of robot fallng on the robot whle deployment and call that falure rate analyss. b) Another assumpton s that the NAGs or the mnes n ths applcaton are statc. Ths s true because n practce all the mnes are statc. In future applcatons, the NAGs could also be dynamc and hence the experment can be repeated for the agent behavor. c) Also, n the mne detecton applcaton, the memory s not used. Ths s because, there s usefulness n rememberng ether the locaton nformaton of the mne or the type of mne tself. In future, we can redefne the applcaton more specfc by employng dfferent functons for dfferent knds of mne. In ths process, memory wll be helpful n rememberng the nformaton about the type of mne that could be useful rather than the locaton nformaton. 9 Concluson Ths research draws a generc model named AISIMAM based on artfcal mmune system applcable to ntellgent mult agents. An applcaton for the model s smulated. The mne detecton and dffuson problem s expermented and the results show that AISIMAM s successful. The motvaton for ths applcaton s that n future the mne detecton can be performed effcently by deployng moble robots that have enough ntellgence, communcaton and coordnaton to detect and dffuse the mnes. To verfy the generalty of the model, more

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