Computational Theory of Mind for Human-Agent Coordination

In everyday life, people often depend on their theory of mind, i.e., their ability to reason about unobservable mental content of others to understand, explain, and predict their behaviour. Many agent-based models have been designed to develop computational theory of mind and analyze its effectiveness in various tasks and settings. However, most existing models are not generic (e.g., only applied in a given setting), not feasible (e.g., require too much information to be processed), or not human-inspired (e.g., do not capture the behavioral heuristics of humans). This hinders their applicability in many settings. Accordingly, we propose a new computational theory of mind, which captures the human decision heuristics of reasoning by abstracting individual beliefs about others. We specifically study computational affinity and show how it can be used in tandem with theory of mind reasoning when designing agent models for human-agent negotiation. We perform two-agent simulations to analyze the role of affinity in getting to agreements when there is a bound on the time to be spent for negotiating. Our results suggest that modeling affinity can ease the negotiation process by decreasing the number of rounds needed for an agreement as well as yield a higher benefit for agents with theory of mind reasoning.</p

Effective early termination techniques for text similarity join operator

Bu çalışma, 26-28 Ekim 2005 tarihleri arasında İstanbul[Türkiye]'da düzenlenen 20. International Symposium on Computer and Information Sciences'da bildiri olarak sunulmuştur.Text similarity join operator joins two relations if their join attributes are textually similar to each other, and it has a variety of application domains including integration and querying of data from heterogeneous resources; cleansing of data; and mining of data. Although, the text similarity join operator is widely used, its processing is expensive due to the huge number of similarity computations performed. In this paper, we incorporate some short cut evaluation techniques from the Information Retrieval domain, namely Harman, quit, continue, and maximal similarity filter heuristics, into the previously proposed text similarity join algorithms to reduce the amount of similarity computations needed during the join operation. We experimentally evaluate the original and the heuristic based similarity join algorithms using real data obtained from the DBLP Bibliography database, and observe performance improvements with continue and maximal similarity filter heuristics.Inst Elec & Elect Engineers, Turkey SectBoğaziçi Üniversites

Multiagent cooperation for solving global optimization problems: an extendible framework with example cooperation strategies

This paper proposes the use of multiagent cooperation for solving global optimization problems through the introduction of a new multiagent environment, MANGO. The strength of the environment lays in itsflexible structure based on communicating software agents that attempt to solve a problem cooperatively. This structure allows the execution of a wide range of global optimization algorithms described as a set of interacting operations. At one extreme, MANGO welcomes an individual non-cooperating agent, which is basically the traditional way of solving a global optimization problem. At the other extreme, autonomous agents existing in the environment cooperate as they see fit during run time. We explain the development and communication tools provided in the environment as well as examples of agent realizations and cooperation scenarios. We also show how the multiagent structure is more effective than having a single nonlinear optimization algorithm with randomly selected initial points

Enacting protocols by commitment concession

Commitment protocols formalize interactions among autonomous, heterogeneous agents, leaving the agents ’ local policies unspecified. This paper studies the problem of agents enacting commitment protocols, which inherently requires that their policies cohere with the given protocols. Specifically, in many important settings, if agents incautiously create and discharge commitments, they can expose themselves to certain risk; conversely, if the agents are (excessively) cautious, a protocol enactment may deadlock. This paper adopts the well-known idea of monotonic concession, but specializes and enhances it with the particular features of commitments. Specifically, this paper formulates inference rules for commitment concession that respect the nature of commitments. Next, it shows how commitments can be systematically revised as the agents incrementally engage each other in enacting their protocol. This paper demonstrates how such rules can be applied in practice, an

Correctness Requirements for Multiagent Commitment Protocols

Abstract. Commitments are a powerful abstraction for representing the interactions between agents. Commitments capture the content of the interactions declaratively and allow agents to reason about their actions. Recent work on multiagent protocols define agent interactions as the creation and manipulation of commitments to one another. As a result, commitment protocols can be executed flexibly, enabling the agents to cope with exceptions that arise at run time. We study the correctness requirements of commitment protocols that are necessary to ensure correct specification and coherent execution. We analyze and formalize various requirements for commitment protocols and draw relations among them. The main contribution of this analysis is that it allows protocol designers to develop correct protocols by signaling possible errors and inconsistencies that can possibly arise at run time. Since the requirements are formal, they can be incorporated in a software tool to automate the design and specification of commitment protocols.