Relaciones relevantes entre las categorías y códigos establecidos

In this additional experiment, we use the same set-up as previously, but compare different pairs of provider selection strategies, so that the pairs are p-ACS/r-SR and p-EB/r-SR, and p-CC/r-SR and p-EB/r-SR (and the same pairs with all other requester selection

0 10 20 30 40 50 60 70 80 90 100 0.3 0.35 0.4 0.45 0.5 0.55

Low Skilled Providers (%)

Average Satisfaction

p−ACS/r−SR p−EB/r−SR

(a) p-ACS/r-SR and p-EB/r-SR

0 10 20 30 40 50 60 70 80 90 100 0.3 0.35 0.4 0.45 0.5 0.55

Low Skilled Providers (%)

Average Satisfaction

p−ACS/r−DB p−EB/r−DB

(b) p-ACS/r-DB and p-EB/r-DB

0 10 20 30 40 50 60 70 80 90 100 0.3 0.35 0.4 0.45 0.5 0.55

Low Skilled Providers (%)

Average Satisfaction

p−ACS/r−ACS p−EB/r−ACS

(c) p-ACS/r-ACS and p-EB/r-ACS

0 10 20 30 40 50 60 70 80 90 100 0.3 0.35 0.4 0.45 0.5 0.55

Low Skilled Providers (%)

Average Satisfaction

p−ACS/r−CCP p−EB/r−CCP

(d) p-ACS/r-CCP and p-EB/r-CCP

0 10 20 30 40 50 60 70 80 90 100 0.3 0.35 0.4 0.45 0.5 0.55

Low Skilled Providers (%)

Average Satisfaction

p−ACS/r−CCG p−EB/r−CCG

(e) p-ACS/r-CCG and p-EB/r-CCG

Figure 9.14: Pairwise comparison of p-ACS with p-EB using all requester selection

strategies in terms of the agents’ average satisfaction.

strategies).

Comparing the performance of p-ACS and p-EB in terms of average satisfaction, p-ACS

achieves higher average satisfaction when competing in the same population as p-EB,

0 10 20 30 40 50 60 70 80 90 100 0.3 0.35 0.4 0.45 0.5 0.55

Low Skilled Providers (%)

Average Satisfaction

p−CC/r−SR p−EB/r−SR

(a) p-CC/r-SR and p-EB/r-SR

0 10 20 30 40 50 60 70 80 90 100 0.3 0.35 0.4 0.45 0.5 0.55

Low Skilled Providers (%)

Average Satisfaction p−CC/r−DB p−EB/r−DB (b) p-CC/r-DB and p-EB/r-DB 0 10 20 30 40 50 60 70 80 90 100 0.3 0.35 0.4 0.45 0.5 0.55

Low Skilled Providers (%)

Average Satisfaction

p−CC/r−ACS p−EB/r−ACS

(c) p-CC/r-ACS and p-EB/r-ACS

0 10 20 30 40 50 60 70 80 90 100 0.3 0.35 0.4 0.45 0.5 0.55

Low Skilled Providers (%)

Average Satisfaction p−CC/r−CCP p−EB/r−CCP (d) p-CC/r-CCP and p-EB/r-CCP 0 10 20 30 40 50 60 70 80 90 100 0.3 0.35 0.4 0.45 0.5 0.55

Low Skilled Providers (%)

Average Satisfaction

p−CC/r−CCG p−EB/r−CCG

(e) p-CC/r-CCG and p-EB/r-CCG

Figure 9.15: Pairwise comparison of p-CC with p-EB using all requester selection

strategies in terms of the agents’ average satisfaction.

selection strategies except r-DB (with which p-ACS achieves similar performance to p- EB), as shown in Figure 9.14. For the pair of strategies p-CC and p-EB, p-CC has similar performance to p-EB across all requester selection strategies, as shown in Figure 9.15.

Therefore, of the provider selection strategies in Hypothesis 2, p-ACS performs better than p-CC in maintaining higher average satisfaction than a strategy using pure service evaluation, p-EB. Even though p-ACS does not consider the quality of service directly when selecting providers, but indirectly through the balance of exchange values, it is able to select those agents providing better quality services.

0 10 20 30 40 50 60 70 80 90 100 0.3 0.35 0.4 0.45 0.5 0.55 Low−skilled Providers (%) Average Satisfaction p−CC/r−SR p−CC/r−DB p−CC/r−ACS p−CC/r−CCP p−CC/r−CCG

(a) Average Satisfaction for p-CC

0 10 20 30 40 50 60 70 80 90 100 0.3 0.35 0.4 0.45 0.5 0.55 Low−skilled Providers (%) Average Satisfaction p−ACS/r−SR p−ACS/r−DB p−ACS/r−ACS p−ACS/r−CCP p−ACS/r−CCG

(b) Average Satisfaction for p-ACS

0 10 20 30 40 50 60 70 80 90 100 0.3 0.35 0.4 0.45 0.5 0.55 Low−skilled Providers (%) Average Satisfaction p−EB/r−SR p−EB/r−DB p−EB/r−ACS p−EB/r−CCP p−EB/r−CCG

(c) Average Satisfaction for p-EB

Figure 9.16: Influence of requester selection strategies on each provider selection

strategy regarding average satisfaction.

The influence of requester selection strategies on the performance of provider selection strategies p-ACS, p-CC and p-EB is detailed in Figure 9.16. It shows that, as the number of low-skilled providers increases, provider selection strategies p-ACS and p-CC have better performance in terms of average satisfaction when using requester selection strategies r-CCP and r-CCG, which consider service quality. Importantly, p-EB is not influenced by requester selection strategies because it uses service evaluation as the selection criterion, which is not influenced by the choices of a requester selection strategy, as opposed to exchange values which are used by p-ACS and p-CC. Thus, of the requester selection strategies in Hypothesis 3, only r-CCP and r-CCG, which take into account service quality in addition to reciprocity, facilitate the selection of available providers offering good quality services, bringing higher average satisfaction for requesters, but

only for the provider selection strategies p-ACS and p-CC.

9.7

Conclusions

In this chapter we have presented an empirical study to evaluate and compare the efficiency of provider and requester selection strategies used by agents in a cooperative system with service variety and resource constraints. The efficiency of provider selection strategies is related to their ability to find providers that perform good quality services, and that are more likely to accept requests. Similarly, the efficiency of requester selection strategies is related to their ability to improve chances of future interactions and their quality when there are resource constraints. These abilities were evaluated through two performance measures: the total number of interactions achieved; and the average satisfaction with received services.

We have argued, in Chapters 7 and 8, that in a non-monetary cooperative system re- questers must take into account reciprocal relationships with providers to find those more likely to accept requests, since when providers are busier or have few available resources, agents requesting services need to compete for available providers. By comparing the total interactions achieved by agents using provider selection strategies considering re- ciprocation with those not taking into account reciprocation, in Experiments 1 and 2 (Sections 9.5.1 and 9.5.2), we showed that, indeed, provider selection strategies that consider reciprocation allow agents to find available providers easily (since they achieve a higher number of total interactions). This ability is more evident when providers have limited resources, as shown in Experiment 2.

In addition to finding providers more likely to accept requests, provider selection strate- gies must identify those providers offering good quality services. Strategies that consider quality of service are more likely to have higher average satisfaction. However, the rel- ative emphasis on the balance of exchange values (represented by p-ACS) as opposed to both service evaluation and reciprocal relationships (represented by the p-CC strat- egy) is important to understand and determine in order for cooperation to be effective. Results in Experiments 3 and 4, in Sections 9.6.1 and 9.6.2, show that the combined criteria p-CC strategy performs well in terms of average satisfaction, and no worse than a pure evaluation-based strategy. More importantly, the exchange values-based p-ACS

performsbetter than the purely evaluation-based strategy (p-EB) because, by balancing

reciprocity and service quality, p-ACS can identify providers that simultaneously provide good services and are more likely to cooperate.

Moreover, achieving more efficient and effective cooperation depends not only on the provider selection strategy but also on the requester selection strategy. For example, the

pure reciprocation dependence-based requester selection strategy (p-DB) has a positive

interactions. Conversely, thecombined criteriarequester selection strategies (r-CCP and r-CCG) improve the performance of corresponding provider selection strategy p-CC in terms of average satisfaction. This demonstrates that it is possible to balance reciprocal interactions with the analysis of the quality of those interactions when providers need to limit requests, in order for agents to operate effectively in heterogeneous cooperative systems.

In summary our experiments have demonstrated the following key aspects.

• Provider selection strategies that identify reciprocal relationships are able to find

providers more likely to reciprocate than those strategies using service quality alone, and are necessary for busy providers with resource limits or an increasing number of requests.

• Provider selection strategies that identify reciprocal relationships, in addition to

service quality, through exchange values, are able to find providers that are, at the same time, more likely to reciprocate and perform the best services, resulting in agents with high satisfaction with received services. Such strategies are necessary when agents operate in systems with many low quality services.

• Requester selection strategies that use dependence alone increase the number of

interactions in which agents engage, thus receiving more services. However, in ex- treme situations in which providers have minimum resource capacities, dependence alone is not enough; an efficient requester selection strategy requires a combination of dependence and exchange values as selection criteria.

Conclusions

In this thesis, we have proposed models, methods and mechanisms with the aim of sup- porting effective cooperation and partner selection for computational entities operating in open cooperative systems with free services. In this chapter, we summarise the key characteristics of each proposed model, method and mechanism, and highlight the re- search contributions resulting from this thesis. We also discuss the limitations of our approaches, and explore possible extensions and alternative applications. With this ob- jective, the chapter starts with a thesis summary in Section 10.1, followed by research contributions in Section 10.2, and limitations in Section 10.3. Future work is presented in Section 10.4, and we finish with concluding remarks in Section 10.5.

10.1

Thesis Summary

The work in this thesis is contextualised in the area of open cooperative distributed systems, in particular, systems in which services are provided free of charge. Computa- tional entities operating in such systems often have different capabilities and interests, and need to cooperate with each other to achieve individual or common goals. Here, the challenges imposed on cooperations are that services are dynamic and provide different levels of quality, and service providers often have bounded resources and may not always be willing to cooperate.

For cooperation to be efficient in this context, entities requesting services must select among providers, first so that the services they receive have good quality, and second so that they find available providers quickly. In addition, entities providing services must have a non-monetary incentive for cooperation, and decide whether to cooperate with others, such that they can manage their computational resources sensibly, improve their chances of future interactions, and terminate cooperations that do not bring any benefit. Given this, we can consider the computational entities that make up such distributed systems as agents interacting in a multi-agent system.

Although there are existing mechanisms in the multi-agent systems and distributed sys- tems literature that address cooperation and partner selection, as discussed in Chapter 2, they have limitations in dealing with specific characteristics of domain applications and of open systems, or do not provide a combined solution for achieving both efficient and cooperative behaviour in the context of open non-monetary cooperative systems. Therefore, we propose in this thesis a set of models, methods and mechanisms that are combined to support effective cooperation in open systems with free services. More specifically, we address the problems of incentivising non-monetary cooperation and of partner selection in open cooperative contexts.

We started by identifying, in Chapter 3, an application scenario in the bioinformatics domain, aiming at a concrete case study. The choice for bioinformatics in particular has dual sides. First, due to the inherent characteristics of service variety in bioinformatics, in which most services are available free of charge and data is inter-related, the benefits that open cooperative distributed systems can bring to this domain are promising (Stein, 2002; Foster, 2005). However, these same characteristics offer additional challenges for developing agents capable of efficient cooperative behaviour.

Indeed, in Chapter 3 we identified in more detail the specific issues that need to be ad- dress so that agents achieve efficient cooperative behaviour in open cooperative systems

with free services, and presented an architecture with four components: a framework

for non-monetary cooperative interactions, which provides non-monetary incentives for

service provision and a means to analyse cooperations; an evaluation method, which is

responsible for evaluating dynamic services; a requester selection mechanism, which is

responsible for decision-making over service provision, and uses information on cooper-

ative relationships and their properties; and a provider selection mechanism, which is

responsible for decision-making over service requests.

The evaluation method for dynamic services, proposed in Chapter 5, aims at measuring the properties of different services, so that agents have a means to identify those with better properties according to their preferences. To achieve this, we developed a general evaluation method using evaluation attributes, result measures for those attributes, and evaluation functions. Here evaluation must be undertaken from the different perspectives of providers and requesters, with agents using the evaluations generated by our method to select among alternative interaction partners. The proposed evaluation method was

demonstrated through application in real bioinformatics services (ms/msservices) used

for protein identification.

In addition to an awareness of service quality, efficient cooperative behaviour in open sys- tems also requires that providers have non-monetary incentives to cooperate and that agents have a means to analyse the cooperations in which they engage. In response, in Chapter 6, we described a computational framework for non-monetary interactions among agents, based on Piaget’s theory of exchange values, but addressing the limita-

tions of Piaget’s theory to provide a computational representation of exchange values, and a computational model that determines how exchange values are acquired, stored, and spent by agents over interactions. As part of the computational model, we use a valorisation system to determine credits and debts, combining an agent’s objective eval- uation of a service (through the evaluation method) with subjective evaluations of that service (through subjective influences). Practical examples of cooperative interactions that show how exchange values can be determined from service evaluation, with agents providing and requesting protein identification services, demonstrated the applicability of the framework.

Based on this, in Chapter 7 we described a provider selection mechanism for agents requesting services. The objective here is to select providers that are, at the same time, more likely to provide good services, and more likely to accept a request, given that they are not obliged to do so. As part of the provider selection mechanism, therefore, we developed an algorithm to select among alternative service providers, and a group of strategies to instantiate it, using quality-related selection criteria (based on previous service evaluations and the balance of exchange values), and persuasion-related selec- tion criteria (based on reciprocal relationships among requester and providers, including the credits, debts and service dependence among them). By combining information on service evaluation and reciprocal relationships, it is possible to develop a selection mech- anism that can help service requesters to find providers of good quality services without spending too much time doing so.

Similarly, in Chapter 8, we developed a request selection mechanism, which aims not only to support flexible decision making over whether to accept requests, but also to allow agents to manage their resources. We developed an algorithm for dynamic decision- making among incoming requests, and a group of strategies to instantiate this algorithm, establishing preference orderings for requesters according to different combinations of the selection criteria defined earlier.

For both of these selection mechanisms, we carried out an empirical study to test their applicability and efficiency, in Chapter 9. In particular, we simulated a cooperative system, with heterogeneous services, and with resource-constrained providers. We com- pared our proposed provider and request selection strategies and showed that agents using reciprocation-based provider selection strategies (using exchange values or depen- dence) are able to find available providers much quicker than those not doing so (using evaluation alone or at random). Moreover, our results showed that, when consider- ing both service quality, through the balance of exchange values, and reciprocal rela-

tionships, agents using theexchange values-based provider selection strategies not only

achieve high proportions of successful interactions and high average satisfaction with received services, but they are also better than the purely evaluation-based provider selection strategy. This is because, by balancing reciprocal relationships and service quality, provider selection strategies can identify providers that are, at the same time,

providing good services and more likely to cooperate. For requester selection, we showed that by considering reciprocal relationships, through a combination of dependence and exchange values, agents can indeed improve their chances of future interaction by in- creasing the performance of provider selection strategies in finding available providers

quicker. Also, our proposed combined strategy with a preference for paying debts not

only helps to improve the chances of future interactions but also improves the quality of those interactions. More importantly, we showed that it is possible to improve the quality of interactions even when providers need to limit requests.