Reyhan Aydoğan

The Fifth Automated Negotiating Agents Competition (ANAC 2014)

In May 2015, we organized the Sixth International Automated Negotiating Agents Competition (ANAC 2015) in conjunction with AAMAS 2015. ANAC is an international competition that challenges researchers to develop a successful automated negotiator for scenarios where there is incomplete information about the opponent. One of the goals of this competition is to help steer the research in the area of multi-issue negotiations, and to encourage the design of generic negotiating agents that are able to operate in a variety of scenarios. 24 teams from 9 different institutes competed in ANAC 2015. This chapter describes the participating agents and the setup of the tournament, including the different negotiation scenarios that were used in the competition. We report on the results of the qualifying and final round of the tournament.

A Graph-BasedWeb Service Composition Technique Using Ontological Information

We investigate Web service composition as a planning problem and use the input-output parameter relations in order to select the constituent services that make up the composite service. Furthermore, we make use of ontological information between the input-output parameters such that a more specific concept can be used instead of a general concept to make the process more flexible. Our proposed approach is based on constructing a dependency graph including the service parameters and Web services themselves. By using this dependency graph, we perform backward chaining starting to search from the desired output parameters, which is in fact the goal, to the available input parameters. In addition to using semantic information through the search, our approach considers non-functional attributes of the services such as service quality. Considering the quality measures, we find the constituent services by making use of depth first search. After finding the required services, our algorithm generates a plan that shows the execution order of each service.

Heuristic-Based Approaches for CP-Nets in Negotiation

CP-Nets have proven to be an effective representation for capturing preferences. However, their use in multiagent negotiation is not straightforward. The main reason for this is that CP-Nets capture partial ordering of preferences, whereas negotiating agents are required to compare any two outcomes based on the request and offers. This makes it necessary for agents to generate total orders from their CP-Nets. We have previously proposed a heuristic to generate total orders from a given CP-Net. This paper proposes another heuristic based on Borda count, applies it in negotiation, and compares its performance with the previous heuristic.

Learning opponent's preferences for effective negotiation: an approach based on concept learning

We consider automated negotiation as a process carried out by software agents to reach a consensus. To automate negotiation, we expect agents to understand their user’s preferences, generate offers that will satisfy their user, and decide whether counter offers are satisfactory. For this purpose, a crucial aspect is the treatment of preferences. An agent not only needs to understand its own user’s preferences, but also its opponent’s preferences so that agreements can be reached. Accordingly, this paper proposes a learning algorithm that can be used by a producer during negotiation to understand consumer’s needs and to offer services that respect consumer’s preferences. Our proposed algorithm is based on inductive learning but also incorporates the idea of revision. Thus, as the negotiation proceeds, a producer can revise its idea of the consumer’s preferences. The learning is enhanced with the use of ontologies so that similar service requests can be identified and treated similarly. Further, the algorithm is targeted to learning both conjunctive as well as disjunctive preferences. Hence, even if the consumer’s preferences are specified in complex ways, our algorithm can learn and guide the producer to create well-targeted offers. Further, our algorithm can detect whether some preferences cannot be satisfied early and thus consensus cannot be reached. Our experimental results show that the producer using our learning algorithm negotiates faster and more successfully with customers compared to several other algorithms.

Alternating Offers Protocols for Multilateral Negotiation

This paper presents a general framework for multilateral turn-taking protocols and two fully specified protocols namely Stacked Alternating Offers Protocol (SAOP) and Alternating Multiple Offers Protocol (AMOP). In SAOP, agents can make a bid, accept the most recent bid or walk way (i.e., end the negotiation without an agreement) when it is their turn. AMOP has two different phases: bidding and voting. The agents make their bid in the bidding phase and vote the underlying bids in the voting phase. Unlike SAOP, AMOP does not support walking away option. In both protocols, negotiation ends when the negotiating agents reach a joint agreement or some deadline criterion applies. The protocols have been evaluated empirically, showing that SAOP outperforms AMOP with the same type of conceder agents in a time-based deadline setting. SAOP was used in the ANAC 2015 competition for automated negotiating agents.

Multilateral Mediated Negotiation Protocols with Feedback

When more than two participants have a conflict of interest, finding a mutual agreement may entail a time consuming process especially when the number of participants is high. Automated negotiation tools can play a key role in providing effective solutions. This paper presents two variants of feedback based multilateral negotiation protocol in which a mediator agent generates bids and negotiating agents give their feedback about those bids. We investigate different types of feedback given to the mediator. The mediator uses agents’ feedback to models each agent’s preferences and accordingly generates well-targeted bids over time rather than arbitrary bids. Furthermore, the paper investigates the performance of the protocols in an experimental setting. Experimental results show that the proposed protocols result in a reasonably good outcome for all agents in a relatively short time.

Heuristics for using CP-nets in utility-based negotiation without knowing utilities

CP-nets have proven to be an effective representation for capturing preferences. However, their use in automated negotiation is not straightforward because, typically, preferences in CP-nets are partially ordered and negotiating agents are required to compare any two outcomes based on a request and an offer in order to negotiate effectively. If agents know how to generate total orders from their CP-nets, they can make this comparison. This paper proposes heuristics that enable the use of CP-nets in utility-based negotiations by generating total orderings. To validate this approach, the paper compares the performance of CP-nets with our heuristics with the performance of UCP-nets that are equipped with complete preference orderings. Our results show that we can achieve comparable performance in terms of the outcome utility. More importantly, one of our proposed heuristics can achieve this performance with significantly smaller number of interactions compared to UCP-nets.

A negotiation approach for energy-aware room allocation systems

This paper addresses energy-aware room allocation management where the system aims to satisfy individuals’ needs as much as possible while concerning total energy consumption in a building. In the problem, there are a several rooms having varied settings resulting in different energy consumption. The main objective of the system is not only finding the right allocations for user’s need, but also minimizing energy consumption. However, the users of the system may have conflicting preferences over the rooms to be allocated for them. This paper pursues how the system can increase user satisfaction while achieving its goals. For that purpose, an adaptation of the mediated single text negotiation model is introduced. The proposal seeks to guarantee an upper bound on energy consumption by pruning the negotiation space via a genetic algorithm, and to take advantage of the negotiation for increasing user satisfaction. Experiments suggest that the adaptations improve the performance.

Reasoning and Negotiating with Complex Preferences Using CP-Nets

Automated negotiation is important for carrying out flexible transactions. Agents that take part in automated negotiation need to have a concise representation of their user’s preferences and should be able to reason on these preferences effectively. We develop an automated negotiation platform wherein consumer agents negotiate with producer agents about services. A consumer agent represents its user’s preferences in a compact way using a CP-net, which is a structure that allows users to order their preferences based on the different value combinations of attributes. Acquiring user’s preferences in a compact way is crucial since it significantly decreases the number of questions to be asked to the user by the consumer agent. We design strategies for consumer agents to reason on and negotiate effectively with the preference graph induced from a CP-net. These strategies are designed to generate deals that are acceptable by the provider and the consumer. We compare our proposed strategies in terms of how well and how quickly they can find desirable deals for the consumer.

The Automated Negotiating Agents Competition, 2010–2015

The Automated Negotiating Agents Competition is an international event that, since 2010, has contributed to the evaluation and development of new techniques and benchmarks for improving the state of the art in automated multi-issue negotiation. A key objective of the competition has been to analyze and search the design space of negotiating agents for agents that are able to operate effectively across a variety of domains. The competition is a valuable tool for studying important aspects of negotiation including profiles and domains, opponent learning, strategies, and bilateral and multilateral protocols. Two of the challenges that remain are how to develop argumentation-based negotiation agents that, in addition to making offers, can inform and argue to obtain an acceptable agreement for both parties; and how to create agents that can negotiate in a human fashion.