Nearly 20% of total energy consumption in the United States is accounted for in heating, ventilation, and air conditioning (HVAC) systems. Smart sensing and adaptive energy management agents can greatly decrease the energy usage of HVAC systems in many building applications, for example by enabling the operator to shut off HVAC to unoccupied rooms. We implement a multimodal sensor agent that is nonintrusive and low-cost, combining information such as motion detection, CO2 reading, sound level, ambient light,and door state sensing. We show that in our live test bed at the USC campus, these sensor agents can be used to accurately estimate the number of occupants in each room using machine learning techniques, and that these techniques can also be applied to predict future occupancy by creating agent models of the occupants. These predictions will be used by control agents to enable the HVAC system increase its efficiency by continuously adapting to occupancy forecasts of each room.

Stackelberg games have been used in several deployed applications of game theory to make recommendations for allocating limited resources for protecting critical infrastructure. The resource allocation strategies are randomized to prevent a strategic attacker from using surveillance to learn and exploit patterns in the allocation. An important limitation of previous work on security games is that it typically assumes that attackers have perfect surveillance capabilities, and can learn the exact strategy of the defender. We introduce a new model that explicitly models the process of an attacker observing a sequence of resource allocation decisions and updating his beliefs about the defender's strategy. For this model we present computational techniques for updating the attacker's beliefs and computing optimal strategies for both the attacker and defender, given a specific number of observations. We provide multiple formulations for computing the defender's optimal strategy, including non-convex programming and a convex approximation. We also present an approximate method for computing the optimal length of time for the attacker to observe the defender's strategy before attacking. Finally, we present experimental results comparing the efficiency and runtime of our methods.

This paper surveys research in robotics in the AAMAS (Au- tonomous Agents and Multi-Agent Systems) community. It argues that the autonomous agents community can, and has, impact on robotics. Moreover, it argues that agents re- searchers should proactively seek to impact the robotics com- munity, to prevent independent re-discovery of known results, and to benefit autonomous agents science. To support these claims, I provide evidence from my own research into multi- robot teams, and from others’.

The paper discusses how a human-robot team can remain “cohesive” while performing under stress. By cohesive the paper understands the ability of the team to operate effectively, with individual members being interdependent-yet-autonomous in carrying out tasks. For a human-robot team, we argue that this requires robots to (1) have an adequate sense of that interde- pendency in terms of the social dynamics within the team, and to (2) maintain transparency towards the human team members in terms of what it is doing, why, and to what extent it can achieve its (possibly jointly agreed upon) goals. The paper re- ports of recent field experience showing that failure in trans- parency results in reduced acceptability of robot autonomous behavior by the human team members. This reduction in acceptability can have two negative impacts on cohesiveness: Humans and robots fail to maintain common ground, and as a result they fail to maintain trust.

Computational models of aggregated social agents have two major faults: (1) inter-individual entrainment is ignored; and (2) rule-sets governing behavior are invariant to history. Together these shortcomings impede our ability to generate realistic models of complex evolving social processes. To illustrate how even simple couplings within an established dyad generates unexpected outcomes, we present our findings from two computer models (agent-based, particle filter) of married couples. With the use of computational modeling, especially when attempting to capture and articulate trajectories of socially aggregated agents, numerous implicit assumptions are made and yet, many if not most, are without an empirical Figure 1: User interface showing parameter sliderbars that foundation. For example, the standard protocol for creating modify interaction characteristics.

Hybrid group autonomy, organizations and teams composed of humans, machines and robots, are important to AI. Unlike the war in Iraq in 2002, the war in Afghanistan has hundreds of mobile robots aloft, on land, or under the sea. But when it comes to solving problems as part of a team, these agents are socially passive. Were the problem of aggregation and the autonomy of hybrids to be solved, robot teams could accompa- ny humans to address and solve problems together on Mars, under the sea, or in dan- gerous locations on earth (such as, fire-fighting, reactor meltdowns, and future wars). “Robot autonomy is required because one soldier cannot control several robots ... [and] because no computational system can discriminate between combatants and innocents in a close-contact encounter.” (Sharkey, 2008) Yet, today, one of the fundamental unsolved problems in the social sciences is the aggregation of individual data (such as preferences) into group (team) data (Giles, 2011) The original motivation behind game theory was to study the effect that multi- ple agents have on each other (Von Neumann and Morgenstern, 1953), known as interdependence or mutual dependence. Essentially, the challenge addresses the ques- tion: why is a group different from the collection of individuals who comprise the group? That the problem remains unsolved almost 70 years later is a remarkable com- ment on the state of the social sciences today, including game theory and economics. But solving this challenge is essential for the science and engineering of multiagent, multirobot and hybrid environments (that is, humans, machines and robots working together).

A principal seeks production of a good within a limited time-frame with a hard deadline, after which any good procured has no value. There is inherent uncertainty in the production process, which in light of the deadline may warrant simultaneous production of multiple goods by multiple producers despite there being no marginal value for extra goods beyond the maximum quality good produced. This motivates a crowdsourcing model of procurement. We address efficient execution of such procurement from a social planner's perspective, taking account of and optimally balancing the value to the principal with the costs to producers (modeled as effort expenditure) while, crucially, contending with self-interest on the part of all players. A solution to this problem involves both an algorithmic aspect that determines an optimal effort level for each producer given the principal's value, and also an incentive mechanism that achieves equilibrium implementation of the socially optimal policy despite the principal privately observing his value, producers privately observing their skill levels and effort expenditure, and all acting only to maximize their own individual welfare. In contrast to popular "winner take all" contests, the efficient mechanism we propose involves a payment to every producer that expends non-zero effort in the efficient policy.

The concept of trust in web services describes the degree of belief that a client or a group of clients have over services functioning satisfactorily and providing the expected results. As services are usually invoked in composition with other services, judging on their trustworthiness gets more complicated, yet computing their trustworthy becomes a desired goal. Existing work only take the trust of each individual service into account, regardless of the context of the composition. They also do not use the data gained from other clients for selecting the most trustful composition and preparing for possible service failures. In our work we first introduce the concept of Combination Reputation, which reflects the commonness and popularity of invoaction of a pair or group of services among other clients. By interpreting the trust and reputation values as subjective probability, we define the Component Trust of the services in the composition, which reflects the degree of belief the client has over components of services performing satisfactorily. We model the web service composition as a Bayesian network and integrate the above trust values into the network and show how to compute the global trust of the composition.

A number of real-world security scenarios can be cast as a problem of transiting an area guarded by a mobile patroller, where the transiting agent aims to choose its route so as to minimize the probability of encountering the patrolling agent, and vice versa. We model this problem as a two-player zero-sum game on a graph, termed the transit game. In contrast to the existing models of area transit, where one of the players is stationary, we assume both players are mobile. We also explicitly model the limited endurance of the patroller and the notion of a base to which the patroller has to repeatedly return. Noting the prohibitive size of the strategy spaces of both players, we develop single- and double-oracle based algorithms including a novel acceleration scheme, to obtain optimum route selection strategies for both players. We evaluate the developed approach on a range of transit game instances inspired by real-world security problems in the urban and naval security domains.

In all of these problems, we have limited with researchers in other disciplines, be "on the ground" security resources which prevent full security coverage with domain experts, and examine real-world constraints at all times; instead, limited security resources must be deployed and challenges that cannot be abstracted away. Together as intelligently taking into account differences in priorities an international community of multiagent researchers, we of targets requiring security coverage, the responses of can accomplish more! the adversaries to the security posture and potential uncertainty over the types, capabilities, knowledge and priorities