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A contract is an agreement over the values that issues should take. In the MMPD, this equates to a slot in the game matrix. After an agreement has been signed, agents enact the contents of the contract and may do so with varying degrees of success. Given the structure of the MMPD and the dominant strategy of a PD, the agents will be tempted to defect by enacting values that are more profitable to themselves. This is illustrated on figure 4.4(a) where agentαtries to exploit agentβ by enacting the contract in such a way that it obtains a higher utility than what has been agreed in the contract, resulting in a lower utility for β.

(a)αdefecting after agreeing withβ. (b) β constraining negotiations (the dotted line) such that agreements are at a higher value and the expected defection still results in an acceptable contract.

Figure 4.4: Agents can retaliate using their trust model to capture defections by constraining future agreements.

Agentβ may then capture such defections using its trust model and alter its subsequent behaviour accordingly. One of the ways in which this can be achieved is by the agent bounding the range of slots in the MMPD that are available for the next agreement (see dotted line in figure 4.4(b)). This may then reduce the amount of loss expected in the next interaction. As shown in figure 4.4(b), if agent α indeed defects by the same degree, the resulting enactment of the contract results in more utility forβ than in the first agreement and this additional utility may compensate to some extent for losses incurred in the first game.

Using the formal definitions in this chapter we can now provide a description of this procedure as follows (the details follow in chapter 5). Assume a trust measure by β’s trust model determines the reliability of α to be such that for a given value of v ∈D_x for an issue x, α will enact values of x lying in the range [ev−_{, ev}+_{]. Therefore, given}

a negotiation range [vmin, vmax], an agent may restrict the range to [vmin0 , vmax0 ] defined as follows: ν ={v|[ev−_{, ev}+_]⊆[v min, vmax]} v0 min = inf{v∈Dx |v∈ν} v0 max = sup{v∈Dx|v∈ν}

The above procedure implies restricting the action set ofαandβ, hence the contract, to only those values where the enactment of the contract can compensate for an expected utility loss due to a possible defection byα. We investigate such a technique as well as other means of adapting an agent’s negotiation behaviour in the IMMPD in chapter 5.

4.3

Summary

In this chapter we provided the main formal definitions that are to be used in our trust and PN models. In particular, we defined contracts which describe what agents can offer during negotiation encounters (i.e. the negotiation object) and the utility function that agents can use to evaluate these contracts. Moreover, we provided a description of the MMPD that agents play by virtue of the relationship existing between their utility functions. In so doing, we define the action set of the negotiating agents. This set can be used to define arguments in the PN model and to assess the level of reliability of an opponent by the agents using the trust model. In the latter case, the trust model can subsequently be used to adjust the agent’s negotiation stance accordingly.

Given these definitions, in the next chapter we describe our CREDIT trust model. This model allows an agent to determine its level of trust in its opponents based on its own confidence in them and their reputation in the society of agents. Based on CREDIT’s trust measure, we further elaborate on the different means that CREDIT provides to adjust the negotiation stance.

CREDIT: A Trust Model based

on Confidence and Reputation

Having reviewed the state of the art models of trust (in chapter 3) and provided the basic definitions in the previous chapter, we now describe our trust model. In particular, we design our trust model as per the general requirements for our negotiation mechanisms detailed in section 1.2 and the particular requirements discussed in relation to other trust models in section 3.3.

To this end, in section 5.1 we first analyse the particular problems that remain with current individual level trust models described in section 3.1 and discuss the techniques we use to solve these problems in our model. Building on this, in section 5.2 we then go on to define the CREDIT trust model. Thus we provide definitions of the context within which agents interact and measures of confidence and reputation that model trust in direct and indirect interactions respectively. Moreover, we provide an algorithm to calculate confidence levels and combine these measures with reputation to generate trust measures. The computational complexity of the algorithm is shown to be linear with respect to the number of past interactions analysed and quadratic with respect to the number of decision variables (here these are fuzzy sets) used to characterise particular levels of reliability. We then show in section 5.3 how CREDIT can be directly used in a bargaining encounter, as per our objectives set in 1.5, to influence agreements reached according to the trustworthiness of the negotiating agents. Section 5.4 empirically evalu- ates CREDIT and shows that, by influencing the negotiation stance, it is indeed effective and efficient in preventing an agent from being exploited in the long run and in dealing with agents which are reliable to a certain degree. Furthermore, in section 5.5 CREDIT is shown to be better than other comparable models in negotiating fruitful contracts with partially unreliable agents. Section 5.6 summarizes the main properties of CREDIT and discusses the main issues arising in integrating it with bargaining mechanisms and the system-level trust models.

5.1

Introduction

As we have seen in chapter 3, in general, in a society of agents, trust evolves as a re- sult of the direct assessment of the performance of contracted agents over a number of interactions (see section 3.1.1 or from the acquisition of information from the environ- ment, including other agents (see sections 3.1.2 and 3.2.2). More specifically, obtaining a useful measure of trust requires assessing an opponent’s performance according to the utility derived from the tasks performed by it (i.e. through direct interactions) and public knowledge about its efficiency and effectiveness in each of these tasks (i.e. in- direct interactions). By considering and combining these two sources (of measures) we believe an agent can better assess the trustworthiness of an opponent, particularly in circumstances where either the private or the public source is not reliable on its own. For example, in cases where an agent has interacted a number of times with its opponent, it will probably rely on1When an agent decides to do so will depend on the context within which it finds itself) on its direct measure of its opponent’s trustworthiness. However, in cases where the opponent is previously unknown, the agent will rely on the publicly available knowledge. In between, the agent may combine both and give more impor- tance to one or the other depending on the number of interactions it has had with its opponent (Sabater and Sierra, 2002). Nevertheless, whichever measure of trust is used the overall aim is the same; namely to provide an indication of how an agent is likely to perform with respect to a given commitment. Thus, if an agent has been known to defect very often in the past, it may not prove reliable in the future. Similarly, if an agent has frequently proven its effectiveness in past interactions, it may be regarded as reliable in future interactions. Naturally, in some cases, both of these assumptions may turn out to be false.

When measuring trust, it is therefore important to consider the context in which the interactions take place. Here we view this context as being mainly captured bynorms

(Conte and Castelfranchi, 1999; Esteva et al., 2001). Such norms equate to the obliga- tions imposed on the interactions by the system and they occur as a consequence of the utterances, roles, and pledges of the interacting agents. For example, it is the norm in the eBay auction to pay for the goods that one has won before they can be delivered and it is required by mobile service providers (in Britain) to allow seven days to their customers to cancel any contract they may have signed. The relationship between trust and norms is as follows. If it is known that agents act according to certain norms which guarantee good performance, then there is no point in an agent increasing its trust in another that performs well since we cannot assume that it would do so without the norm applying. However, if no norms or rules force agents to behave well (i.e. there exists a possibility to renege), then trust should be increased in an agent which lives up to its 1_{The agent may decide to rely on its own measure of trustworthiness depending on the context. For}

example, if the agents interact many times in one day and then meet after one month or a year, the direct measure of trust may have become obsolete if the environment is dynamic, while if the environment is static, it may still rely on its own measure after a few days or even a month.

commitments (or decreased if it reneges). Thus, the trust value for a specific agent for a specific task should take into account the potential risk (associated with the task in question) in a contract given information about the norms within which the contract is enacted (Marsh, 1994). This follows from the fact that cooperating under high potential losses (i.e. when no norms enforce good behaviour) shows greater trustworthiness than otherwise (Yamagishi et al., 1998). For example, if no penalty applied for late delivery (i.e. there is no norm regarding delivery times), a seller delivering at the agreed time is deemed more trustworthy than when a harsh penalty applies (i.e. there is a norm that guarantees delivery times).

In general, extant trust models fail to capture all the above-mentioned basic factors in deriving trust (see chapter 3 for more details). In particular, while some of these models devise trust using arbitrary equations that do not take degrees of efficiency into account (see section 3.1.1.2), others simply assume that the trust measure is readily available from the system (see section 3.1.2.1). Moreover, those models which do analyse the performance of opponents fail to consider the norms of the environment which foster good behaviour (L´opez y L´opez et al., 2002; Esteva et al., 2001). Finally, in most applications, extant trust models only use trust in choosing interaction partners and neglect the fact that trust can also be used to adapt the behaviour of an agent towards its opponent at negotiation time (Fisher and Ury, 1983).

Against this background, this chapter develops and evaluates a novel computational trust model (called CREDIT -Confidence andREputationDefiningInteraction-basedTrust) that rectifies these shortcomings. Specifically, we show that by taking into account its past experience (from direct interactions) and information gathered from other agents (indirect interactions), an agent can build up beliefs about how trustworthy a contracted agent is likely to be in meeting the expected outcomes of particular contract issues (e.g. delivering goods on time or delivering high quality goods). In this respect, we conceive of two ways of assessing trustworthiness: (i)Confidencederived (mainly) from analysing the result of previous interactions with that agent, and (ii) Reputation acquired from the experiences of other agents in the community through gossip or by analysing signals sent by an agent. Both measure the same property; that is, the opponent’s believed reliability in doing what it says it will regarding particular issues of a contract. In CREDIT both measures rely on (probabilistic) estimations of utility variation in the current contract, based on an agent’s past experiences as mentioned and experiences of other agents, which are, in turn, used to evaluate the performance of an opponent by means of a small number of fuzzy sets defining different typical behaviours (in terms of utility variations).2 _{The computational complexity of the model is linear with respect to} 2_{Fuzzy sets are here used to characterise the vague perception of the performance of an opponent and}

to provide agents with a high-level of abstraction means of assessing the extent to which an opponent satisfies the issues of a contract. Thus an opponent may be characterised as satisfying with a high degree the (graded/fuzzy) property of ‘delivering-on-time’ and a with a low degree the (graded/fuzzy) property of ‘selling-high-quality’, to denote that it is expected to deliver on time and sell goods of relatively poor quality.

the number of past interactions analysed and quadratic in the number of fuzzy sets (in the worst case) in its incremental complexity (i.e. as new interactions occur). Finally, we show how CREDIT can be used in an agent’s decision making mechanism in order to minimise risk in interactions by influencing the selection of an interaction partner and the negotiation of contracts.

Set against the requirements of our negotiation mechanisms (which need a technique to model an agent’s reliability and honesty in terms of trust) and the challenges that arise in developing such a model (see section 3.3), the work described in this chapter advances the state of the art in the following ways. First, we use the norms of the environment as a key factor in evaluating the trust of opponents. In so doing, we prevent agents from trusting those opponents that are only performing well because of the prevailing norms. Second, we show how fuzzy sets are a very useful tool to describe an agent’s probabilistic estimation of its opponent’s effectiveness and provides a common ontological basis that permits a combination of this estimation with other agents’ estimations. Third, we show how both confidence and reputation measures can be used to develop a measure of trust that is adapted to the environment in which the agents interact and, moreover, how this measure can be adapted over time to become more accurate as more information becomes available. Fourth, we show how CREDIT allows interacting agents, with different norms, to negotiate those issues for which they have different expected values (guided by the norms) and avoid negotiating over those issues for which they have coherent expectations. This, in turn, minimises losses and saves negotiation time. Fifth, we show how trust can be used to adjust the stance that an agent takes during negotiation so as to minimise the utility loss incurred when it believes its opponent is likely to defect by different degrees from a signed contract.