Heterogeneous temporal probabilistic agents

To date, there has been no work on temporal probabilistic agent reasoning on top of heterogeneous legacy databases and software modules. We will define the con-cept of a heterogeneous temporal probabilistic (HTP) agent. Such agents can be built on top of existing databases, data structures, and software code bases without explicitly accessing the internal code of those systems and can take actions compat-ible with a policy or operating principles specified by an agent developer. We will develop a formal semantics for such agents through the notion of a feasible tem-poral probabilistic status interpretation (FTPSI for short). Intuitively, an FTPSI specifies what all an HTP agent is permitted/forbidden/obliged to do at various times t. As changes occur in the environment, the HTP agent must compute a new FTPSI. HTP agents continuously compute FTPSI’s in order to determine what they should do and hence, the problem of computing FTPSI’s is very important. We give a sound and complete algorithm to compute FTPSI’s for a very large class of HTP agents called strict HTP agents. In a given state, many FTPSI’s may exist. These represent alternative courses of action that the HTP agent can take. We pro-vide a notion of an optimal FTPSI that selects an FTPSI optimising an objective function and give a sound and complete algorithm to compute an optimal FTPSI.

Representing and Integrating Multiple Calendars

Whenever humans refer to time, they do so with respect to a specific underlying calendar. So do most software applications. However, most theoretical models of time refer to time with respect to the integers (or reals). Thus, there is a mismatch between the theory and the application of temporal reasoning. To lessen this gap, we propose a formal, theoretical definition of a calendar and show how one may specify dates, time points, time intervals, as well as sets of time points, in terms of constraints with respect to a given calendar. Furthermore, when multiple applications using different calendars wish to work together, there is a need to integrate those calendars together into a single, unified calendar. We show how this can be done. (Also cross-referenced as UMIACS-TR-97-12

Secure Agents

With the rapid proliferation of software agents, there comes an increased need for agents to ensure that they do not provide data and/or services to unauthorized users. We first develop an abstract definition of what it means for an agent to preserve data/action security. Most often, this requires an agent to have knowledge that is impossible to acquire --- hence, we then develop approximate security checks that take into account, the fact that an agent usually has incomplete/approximate beliefs about other agents. We develop two types of security checks --- static ones that can be checked prior to deploying the agent, and dynamic ones that are executed at run time. We prove that a number of these problems are undecidable, but under certain conditions, they are decidable and (our definition of) security can be guaranteed. Finally, we propose a language within which the developer of an agent can specify her security needs, and present provably correct algorithms for static/dynamic security verification. (Also cross-refernced as UMIACS-TR-99-62

Evaluating practical negotiating agents: Results and analysis of the 2011 international competition

This paper presents an in-depth analysis and the key insights gained from the Second International Automated Negotiating Agents Competition (ANAC 2011). ANAC is an international competition that challenges researchers to develop successful automated negotiation agents for scenarios where there is no information about the strategies and preferences of the opponents. The key objectives of this competition are to advance the state-of-the-art in the area of practical bilateral multi-issue negotiations, and to encourage the design of agents that are able to operate effectively across a variety of scenarios. Eighteen teams from seven different institutes competed. This paper describes these agents, the setup of the tournament, including the negotiation scenarios used, and the results of both the qualifying and final rounds of the tournament. We then go on to analyse the different strategies and techniques employed by the participants using two methods: (i) we classify the agents with respect to their concession behaviour against a set of standard benchmark strategies and (ii) we employ empirical game theory (EGT) to investigate the robustness of the strategies. Our analysis of the competition results allows us to highlight several interesting insights for the broader automated negotiation community. In particular, we show that the most adaptive negotiation strategies, while robu

Agents dealing with Time and Uncertainty

Situated agents in the real world need to handle the fact that events occur frequently, as well as the fact that the agent typically has uncertain knowledge about what is true in the world. The ability to reason about both time and uncertainty is therefore very important. In this paper, we develop a formal theory of agents that can reason about both time and uncertainty. The theory extends the notion of agents described in [10, 21] and proposes the notion of temporal probabilistic (or TP) agents. A formal semantics for TP-agents is proposed - this semantics is described via structures called feasible TP-status interpretations (FTPSI's). TP-agents continuously evaluate changes (in the state of the environment they are situated in) and compute appropriate FTPSI's. For a class of TP-agents called positive TP-agents, we develop a provably sound and complete procedure to compute FTPSI's

Using focal point learning to improve tactic coordination in human-machine interactions

We consider an automated agent that needs to coordinate with a human partner when communication between them is not possible or is undesirable (tactic coordination games). Specifically, we examine situations where an agent and human attempt to coordinate their choices among several alternatives with equivalent utilities. We use machine learning algorithms to help the agent predict human choices in these tactic coordination domains. Learning to classify general human choices, however, is very difficult. Nevertheless, humans are often able to coordinate with one another in communication-free games, by using focal points, “prominent ” solutions to coordination problems. We integrate focal points into the machine learning process, by transforming raw domain data into a new hypothesis space. This results in classifiers with an improved classification rate and shorter training time. Integration of focal points into learning algorithms also results in agents that are more robust to changes in the environment.

Temporal Agent Programs

The \agent program" framework introduced by Eiter, Subrahmanian and Pick (Articial Intelligence, 108(1-2), 1999), supports developing agents on top of arbitrary legacy code. Such agents are continuously engaged in an \event occurs! think ! act! event occurs : : : " cycle. However, this framework has two major limitations: (1) all actions are assumed to have no duration, and (2) all actions are taken now, but cannot be scheduled for the future. In this paper, we present the concept of a \temporal agent program" (tap for short) and show that using taps, it is possible to build agents on top of legacy code that can reason about the past and about the future, and that can make temporal commitments for the future now. We develop a formal semantics for such agents, extending the concept of a status set proposed by Eiter et al., and develop algorithms to compute the status sets associated with temporal agent programs. Last, but not least, we show how taps support classical negotiation method..