A Task Domain for Combining and Evaluating Robotics and 

Cognitive Modeling Techniques

Nicholas L. Cassimatis, J. Gregory Trafton, 

Alan C. Schultz, Magdalena D. Bugajska, and William Adams 

Naval Research Laboratory, Code 5515 and 5513 

4555 Overlook Ave. SW 

Washington, DC 20375-5337 

ABSTRACT 

Building systems that integrate different artificial intelligence 

techniques to achieve a higher level of total intelligence is very 

difficult. In order to build integrated systems, simplifying 

assumptions or abstractions are usually made when working in a 

specific domain. As a result of these assumptions and abstractions, 

the proper evaluation of integrated artificial intelligence techniques 

can be quite challenging. We suggest that the domain of hide and 

seek is a particularly well-suited task for integrating robotics and 

higher-level reasoning mechanisms such as computational cognitive 

modeling. Three different instantiations of integrated systems in the 

“hide and seek” domain, which combine cognitive-level algorithms 

with lower level algorithms for perception and navigation are 

discussed. 

KEYWORDS: Robotics, Cognitive Architectures, Cognitive 

Modeling, Performance Metrics 

1. INTRODUCTION 

How do we build intelligent systems, and evaluate the 

underlying algorithms? There are, generally, two main ways 

of building intelligent systems. The first possibility is to focus 

on a relatively small sub domain and build a system or set of 

algorithms that solve problems in that sub domain very well. 

Working in this manner generally leads to very efficient 

methods of solving a relatively narrow set of tasks. There are 

many researchers (including some of the current authors [1]) 

who build these kinds of highly specific systems. The second 

possibility is to build complex systems that solve a larger class 

of problems but that may be less efficient at each task and 

perhaps at the whole task. There are some researchers 

working on these kinds of problems (e.g. [10]), but building 

integrated systems is very difficult for a number of reasons. 

First, the individual techniques are developed using 

different assumptions about their use (e.g., the input/output 

relationship with the environment they are in). Second, 

because most systems are designed for different domains, 

combining two techniques often involves modifying and 

adding new domain specific elements to the design. Finally, 

because each research group’s assumptions and domain are so 

unique, each new group or project must reinvent machinery 

that is relatively incidental to their main interest. For 

example, a researcher trying to develop probabilistic reasoning 

techniques to aid in robot navigation must spend considerable 

effort acquiring and configuring a robotics platform with the 

appropriate sensors and actuators to test possible new 

techniques. 

Each of the above integration issues makes it difficult to 

evaluate the effectiveness of any new technique or system. It 

is almost impossible to compare two techniques when one 

assumes video input from the environment and the other 

assumes sonar; or when one is designed for an office 

navigation task and the other for air traffic control. 

Finally, the structural difficulties of integrating various 

techniques may encourage researchers to ignore or abstract 

away from difficult issues that hold back the field’s progress. 

For example, there is concern [5] that when people working 

on “high-level” artificial intelligence techniques abstract away 

from perception and mobility issues, or when people working 

on perceptual and mobility techniques ignore high-level 

inference, they are actually ignoring the true substance of 

intelligence which lies at the interface between the two. 

In Section 2, we present the task of hide and seek as a 

particularly well-suited task for integrating and evaluating 

artificial intelligence techniques. Section 3 describes three 

different instantiations of integrated systems, which combine 

cognitive-level algorithms with lower level algorithms for 

perception and navigation, and which use the hide and seek 

domain. 

2. THE HIDE AND SEEK DOMAIN 

In order to address these evaluation and integration issues, we 

are organizing a substantial amount of our research around the 

“hide-and-seek” task domain. This domain is forcing us to 

face the difficult integration problems between “high-level” 

cognitive architectures (for example, ACT-R [2] and 

Polyscheme [7]) and systems (such as SAMUEL [9]) for 

sensing and moving in a physical environment. In this 

section, we describe how using a robotic platform in this 
domain allows us to study a surprisingly wide range of issues 

in intelligence, perception and mobility. 

2.1. Perception and Mobility 

Agents that engage in hide-and-seek obviously cannot avoid 

the need to address a wide range of problems in perception 

and mobility. To find their targets, for example, agents must 

be able to identify (object recognition) and move towards their 

targets (path planning) without damaging the environment 

(obstacle avoidance). More generally, any information agents 

can gather perceptually will help them seek and navigate to 

their targets and the more efficiently they navigate to the 

target, the better they will be at hide-and-seek. 

Figure 1. The robot that emerges from behind the screen can be 

the same robot that went behind the screen from the left because 

there was nothing behind the screen to block its motion. The 

barrier did not go behind the screen until after the ball did. 

2.2. Temporal reasoning 

In order to succeed at hide-and-seek, agents must perceive and 

reason about events that occur during various temporal 

intervals. The relations of those intervals among each other 

are important for predicting the outcomes of events and 

therefore the locations of objects that an agent might be 

seeking. Figure 1 shows a simple example of this. In the 

figure, a robot rolls behind an occluding screen and then a 

robot that looks the same rolls out. Next, someone places a 

large barrier behind the screen. Because the barrier was 

placed there after the robot-rolling event, you can assume that 

the space behind the screen was empty during the robotrolling event and that the robot that emerged from the screen 

is the same as the robot that moved behind the screen. 

Figure 2 presents the same scenario, except that the robot 

rolls behind the screen immediately after someone put the 

barrier behind the screen. In this case the robot that emerged 

from the screen cannot be the same as the robot the rolled 

behind the screen because this it did not have time to go 

around the barrier and it could not go through the barrier. 

Figure 2. One knows that the robot that rolls out to the right is 

different from the robot that rolls in from the left because the 

barrier behind the screen would keep the left robot from rolling 

out. 

In these very simple, illustrative cases and in more 

complex situations such as hide-and-seek, the task requires 

agents to make many temporal inferences in order to keep 

track of seeker or target agents and objects. 

2.3. Logical deduction, falsification, default 

reasoning and explanation 

Researchers using logical approaches to artificial intelligence 

have encountered many difficult issues regarding deduction, 

falsification, default reasoning and explanation, and they have 

constructed many sophisticated logical theories to deal with 
them. The following example shows that even the simplest 

physical interactions involve these issues. 

In Figure 3a, a ball rolls towards a screen. In Figure 3b, 

it rolls behind the screen, but in 3c it fails to emerge from 

behind the screen and an object that blocked it is posited 

behind the screen. One can crudely formalize the inference 

that the ball should come out of the screen thus: 

At(ball, left-of-screen, t1) ^ 

Moving(ball, right, t1) ^ 

Empty(behind-screen) 

Æ 

At(ball, behind-screen, t2) ^ 

Moving(ball, right, t2). 

At(ball, behind-screen, t2) ^ 

Moving(ball, right, t2) ^ 

Empty(behind-screen) 

Æ 

At(ball, right-of-screen, t3) ^ 

Moving( ball, right, t3 ).

The inference that the ball emerges from the screen depends 

on the assumption that: 

Empty(behind-screen).

 

When the ball fails to come out from the screen, you infer that 

the proposition, 

Empty(behind-screen), 

is not true and that there must be something behind the screen 

blocking the ball: 

At(something, behind-screen, t2) ^ 

something != ball.

Many traditional issues from the formal logical study of 

intelligence arise here: what can you assume and why; what 

does it take to falsify an assumption; when there is more than 

one explanation for an event; which do you chose; etc. These 

are the usual issues surrounding explanation and default 

reasoning and they also occur whenever you try to build an 

effective hide-and-seek system. 

2.4. Belief revision and reason maintenance 

Any system that reasons in almost any nontrivial domain must 

often infer or assume facts that it must later revise. Because 

the system could have inferred more facts based on the 

originally assumed fact, revising its belief about the original 

fact is much more complicated than simply retracting it [8]. 

The system must retract all beliefs it inferred using the 

original fact that are not otherwise justified. Building systems 

that can revise their beliefs correctly has been a challenge for 

artificial intelligence researchers, even for those trying to 

build good models of common sense physical interactions. 

Figure 3. The ball rolls behind the screen (A), but does not roll 

out (B). There must be an object behind the screen that blocked 

it (C). 

 

Consider an example. Figure 4a shows a scene where a 

screen occludes a table. A block is dropped above the table, it 

falls behind the screen and you infer that it comes to rest on 

the table. Then, when you are told that there is not just one 

table, but that there are two separated tables, as in 4b, you 

must revise your belief about where the ball went when it fell 

behind the screen. In this case, you assume it fell on the floor. 

In general, in order to ascertain the location of any object, 

an intelligent system must make inferences about the object’s 

location, which often depend on provisional information. For 

a system playing hide and seek, if the system spends time 

waiting for or attempting to acquire more definite information, 

the target would have more time to get away. Thus, any 

system that engages in the hide-and-seek task must be able to 

revise provisional beliefs and inferences that followed from it. 

2.5. Planning, searching, problem solving 

Events often have more than one possible outcome and 

systems can usually execute more than one action at any given 

time. The sequence of possible actions and/or inferences 

about event outcomes creates a huge “problem space” of 
possible world states and an intelligent system must choose a 

sequence of actions and/or inferences to achieve an adequate 

state. 

Figure 4. If, as appears in A, there is only one block behind 

the table, then you infer that it came to rest on the table. When 

you learn instead that there are two tables with a gap between 

them, as in B, you infer that the block fell through the gap and 

rests on the floor. 

The need to search through problem spaces is most 

obvious in the hide-and-seek task when it involves robot 

mobility issues. Many algorithms for planning complex paths 

involve creating a visibility or region graph of the space and 

then searching the graph using traditional artificial intelligence 

search techniques. 

Robots designed for the hide-and-seek task, however, 

need planning and search for much more than mobility alone. 

In the following example, we demonstrate that tracking the 

path of a simple ball can require searching through problem 

spaces that involve more than just the location of the ball. 

Figure 5 illustrates a simple physical interaction that requires 

backtracking search. Behind the screen in Figure 4 are two 

buckets. On the left, bucket A is filled with water and on the 

right, bucket B is full of hot coals. Figure 4 also shows a ball 

falling behind the screen. The ball is white and shaped 

roughly like a ping-pong ball, though it may be made of 

plastic or rubber. You see the ball fall behind the screen, 

though you neither see nor hear any further sights or sounds 

because the ball is too light to have dislodged anything and the 

screen masks soft noises. If your task is to figure out if the 

ball fell into bucket A or B, you might imagine that the bucket 

fell into bucket B and infer the consequences. To infer the 

consequence of landing in bucket B, you need to know if the 

ball is rubber or if it is plastic. You can imagine that it is 

rubber, infer that you would smell burning rubber, sense that 

you do not smell anything burning and therefore conclude that 

the ball is not rubber if it fell in B. Likewise, you can infer 

that the ball is not plastic if it fell into B because when you 

imagine a rubber ball lying in burning coals, you imagine a 

certain smell that you do not perceive. So if the ball fell 

behind B, it is neither rubber nor plastic. But you know it was 

one of these, so you know that the ball did not fall into B, but 

instead fell into A. 

Similarly, the hide-and-seek task requires a broad array of 

search abilities. 

Figure 5. Bucket A is filled with water and bucket B is filled 

with hot coals. The ball falls into one of the two buckets. 

2.6. Probabilistic inference 

In many instances of hide-and-seek where events have more 

than one possible outcome, some are more likely than others. 

Seeking a target efficiently is often difficult when a scenario 

involves several possible series of outcomes, because the 

seeker must decide which of the many outcomes is most 
likely. Attempts to make such decisions are often called 

“uncertain reasoning” or “probabilistic inference”. 

Imagine an example like the last one, with the only 

difference being that you know more about the probabilities of 

each uncertainty. Bucket A takes twice the area of bucket B 

and the odds that the ball is plastic are 5:1. You are certain 

that you did not hear a splash, but are uncertain whether you 

smell any new smells. What are the odds that the ball is in A 

and what are the odds that it is in B? 

This example shows that in order to keep track of the 

most likely positions of targets, agents must engage in 

probabilistic inferences. Similarly, hide and seek requires 

similar reasoning. 

2.7. Social reasoning, communication and humanmachine interaction 

When a seeker agent is attempting to find an autonomous 

intelligent agent, it must be able to reason about that agent's 

mental state. Depending on the kind of target agent, this 

implies that competent behavior in the hide-and-seek task 

requires thought about emotions, beliefs, desires, personality 

traits, etc. 

When a team of seeker agents is searching for a target, 

then each team member must be able to communicate with 

other team members in order to coordinate their behavior and 

execute a coherent search strategy. Depending on the 

communications abilities of the seeker agents, this could 

involve language use at all levels: speech recognition to 

decompose the acoustic signal from other agents into words, 

syntax and semantics to determine the meaning of the words 

and pragmatics to understand how the other agent(s) intend an 

utterance to fit into the larger joint seeking joint project. 

When the seeking team contains both humans and robots, 

then the hide-and-seek task becomes a medium for studying 

robot and machine interaction with humans. 

We believe that the full array of research issues in social 

reasoning, communications and human-machine interaction 

can be studied in the hide-and-seek domain. 

2.8. Putting it all together 

Our claim is that hide and seek is an excellent domain with 

which to study intelligence using integrated systems. We have 

presented several needed behaviors that seeker and target 

agents must have. We now go through a simple example of 

playing hide and seek to outline where each of these behaviors 

is needed. Bolded phrases below correspond to the behaviors 

we have discussed earlier. 

Let us assume that we will play hide and seek with a 

robot. The robot will be the seeker agent ("It") first and 

search for the target in a room full of boxes, tables, and desks. 

The target initiates the game with the robot by talking to it 

(communicating with it, using human robot interactions, 

probably language generation and language 

comprehension). Next the robot counts to 10 and starts 

searching for the target agent. The robot must move around 

the environment in the room while avoiding obstacles 

(perception and mobility). The robot may also draw on its 

past experience playing hide and seek to determine that some 

places are better to hide behind and search those places first 

(probabilistic inference). 

If the robot searches behind a box first, it may then 

assume that the target agent will not be there later (unless it 

moved) (temporal reasoning). If the robot then searches the 

entire room and does not find the target agent, it may reason 

that the target must have moved to a different (previously 

searched) hiding place while the robot was searching for it 

(logical deduction, falsification, default reasoning, and 

explanation). The robot must then decide to search the room 

again and re-check positions that it had already searched 

(belief revision and reason maintenance). The robot may 

decide that if the target agent can move around, it should 

search the room in such a way that allows it to see the 

maximum (or most likely) places the target agent would move 

to (planning, searching, problem solving). Once the robot 

finds the target agent, it must tell the target that it was found 

and perhaps even give it some feedback on how good the 

hiding behavior was (social reasoning, communication, and 

HRI). 

2.9. Hide and seek in the real world 

Many real world tasks are instantiations of the basic hide and 

seek domain. In the military, there are missions that directly 

require these skills, including ISR (Intelligence, Surveillance 

and Reconnaissance), RSTA (Reconnaissance, Surveillance, 

and Target Acquisition), and special operations including 

concealment. In non-military domains, hide and seek can be 

found in areas as diverse as Urban Search and Rescue, and 

inspection of facilities (e.g., searching nuclear containment 

facility and superfund sites). 

3. INTEGRATED SYSTEMS 

We now describe the three intelligent systems where highlevel algorithms in the form of computational cognitive 

models are integrated with low-level perception and mobility 

algorithms, and which use various hide and seek domains. 

Available results will be briefly described (and the reader 

directed to appropriate papers for complete results). 

3.1. A hybrid reactive/cognitive architecture for 

micro-air vehicles 

We have developed a hybrid cognitive-reactive system that 

combines more traditional reactive, stimulus-response (S-R) 

behaviors with cognitive models [4][12]. In this work, we 

merge a cognitive model and a reactive system into a control 

system for autonomous vehicles. For this study, the system 
integrates SAMUEL, an evolutionary algorithm-based rule 

learning system [9] with ACT-R, a computational cognitive 

architecture [2]. In our hybrid system, the learning algorithm 

handles reactive aspects of the task and provides an adaptation 

mechanism, and the cognitive model handles the higher-level 

cognitive aspects such as planning and reasoning. The 

cognitive model also provides cognitive realism of the 

behavior. 

Our hybrid controller was implemented for a 

simulated distributed micro air vehicle task. In the 

MAV task, group of vehicles cooperate to perform 

reconnaissance and surveillance, a version of the 

“seeking” task. We assumed each vehicle could detect 

certain ground features below the vehicle and obstacles, 

including other MAVs, within a defined range to the 

dies of the MAV. As a group, the MAVs needed to 

maximize the information gain about the ground 

features, concentrating on areas of more importance, 

and minimizing duplication of effort. In previous work, 

we successfully used genetic algorithms to evolve MAV 

control rule sets that could accomplish the above 

surveillance task [3][13]. 

The cognitive model implemented in ACT-R was 

based on the data collected during human-subject 

experiments performed at NRL and described in greater 

detail in [12]. In those experiments, the human 

operators would control the MAVs by directing them to 

goal locations using a point-and-click interface to the 

simulator. In this study, ACT-R, just like a human 

operator, was responsible for providing 2D goals to 

individual MAVs based on the current perception of the 

world. ACT-R’s perception of the environment was 

closely matched to the perception of the human 

operator. ACT-R could “see” the position and state of 

all MAVs, and the position and value of discovered 

regions of interest. 

SAMUEL was used to evolve stimulus-response 

rules to perform the collision-free reactive navigation 

behavior for the simulated MAVs. Each MAV used the 

same behavior evolved by SAMUEL in conjunction with 

the goals provided by ACT-R to safely navigate to a 

specified location. The current MAV sensor information 

is mapped to the conditions of the stimulus-response 

rules. The action of the rule that is activated specifies 

the action of the vehicle. 

We found the performance of the hybrid controller to be 

comparable to the performance of the human controller, while 

allowing more vehicles to be controlled with fewer collisions. 

The model seems to capture some of the human's behavior and 

performance, while it also allows for higher levels of 

reactivity, which the humans were not able to handle. This 

suggests that our hybrid system is adequately modeling the 

humans' high-level cognitive functions, and also the difficult 

low-level reactive aspects. 

3.2. Polyscheme 

In order to study how to integrate multiple, seemingly 

incompatible, inferential and representational techniques, we 

used the Polyscheme cognitive architecture to develop the S6 

system [7] that reasons about simple physical events that it 

perceives. This was especially helpful in understanding how 

high-level inference and planning techniques might combine 

with and help perceptual algorithms. 

S6 views interactions in a simple physical world through 

a 2-dimensional projection of that world. S6 keeps track of 

the identity of objects, infers the character and existence of 

events it cannot see, predicts the outcome of events, explains 

events and nonevents and revises its inferences when it 

receives new information. S6 successfully reasons about 

many scenarios researchers present to infants and young 

children in order to study their knowledge of the physical 

world. 

S6 combines specialized representation and inference 

techniques for identity, time, events, causality, space and paths 

to successfully deal with a wide range of situations. The 

knowledge representation schemes S6 uses include scripts, 

frames, logical propositions, neural networks and constraint 

graphs. The inference schemes S6 implements include script 

matching, rule matching, backtracking search, neural network 

propagation and counterfactual reasoning. 

3.3. A learning cognitive model for playing the 

game of hide and seek 

In our efforts to add cognitive models for higher level 

reasoning to traditional mobile robotics control, and to 

demonstrate the idea that more effective human-robot 

interactions are possible by using these computational 

cognitive models, we are modeling hide and seek behaviors in 

people, and using these to control a robot. 

We have built a simple computational cognitive model of 

hide and seek. The model is based on a case study of a 3.5 

year old learning to play hide and seek, specifically the 

learning that occurs as the child learns good and poor places to 

hide. The computational cognitive model is built within the 

ACT-R framework [2] and models the reasoning the child 

goes through as she plays and tries different hiding places. 

This is a very difficult task because there is very little 

feedback, very few trials, and very few suggestions. 

The child went from hiding in a room with her eyes shut 

("if I can't see you, you can't see me" strategy) to hiding under 

an upholstered chair in a different room. The model currently 

captures aspects of the child's learning by building a schematic 

representation of hiding and learning that some places are 

good to use as hiding places (e.g., under is good if the object is 

opaque; hiding under a piano is bad). The model also uses a 
simple ontology to reason about hiding given few suggestions 

("Do not hide out in the open") and limited feedback ("You 

hid in a good place"). We are currently in the process of 

putting this computational cognitive model on a robot to play 

a simple game of hide and seek. 

On the robot, perception is handled with a simplified 

perception model. Each object in the room is “labeled” with a 

color target whose color indicates the class of the object (e.g. 

desk or chair), and whose size indicates the approximate size 

of the object (so that the model can determine if it is big 

enough to hide behind. A color camera and a color blob 

detection algorithm [6] are used to find suitable objects to hide 

behind. 

Low-level mobility of the robot is handled by a system 

that combines reactive navigation and collision avoidance, 

explicit path planning, map learning, and localization. This 

system is described in detail in [11]. 

4. GENERAL COMMENTS, FUTURE WORK 

The proper evaluation of integrated artificial intelligence 

techniques can be quite challenging. In this paper, we 

presented the domain of hide and seek as a particularly wellsuited task domain for evaluating the integration of low-level, 

reactive algorithms with higher-level reasoning mechanisms. 

Three different instantiations of integrated systems that 

combine cognitive-level algorithms with lower level 

algorithms for perception and navigation, were described. 

We continue to push the integration of computational 

cognitive models into our systems. We believe that 

incorporating cognitively plausible behavior will permit more 

natural interactions between humans and robots. Using 

computational cognitive architectures and cognitive models 

can ease the ways in which robots communicate with their 

human team members, and vice versa. We have been 

exploring the addition of cognitive models for two goals. 

First, allowing the robot to use the same representations and 

qualitative reasoning as the human will allow for more 

effective and efficient communication. Second, endowing the 

robot with behaviors based on cognitive models of human 

performance allows the robot to exhibit behaviors that are 

similar to how a human might perform a task, thereby 

enhancing social human-robot interaction. Not only does this 

improve interactions with the robot’s human team members, 

but is also critical for robots that need to interact with 

bystanders. We will test these arguments in future research. 

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